 Research
 Open Access
 Published:
An advanced hybrid metaheuristic algorithm for solving small and largescale engineering design optimization problems
Journal of Electrical Systems and Information Technology volume 8, Article number: 10 (2021)
Abstract
An advanced hybrid algorithm (haDEPSO) is proposed in this paper for small and largescale engineering design optimization problems. Suggested advanced, differential evolution (aDE) and particle swarm optimization (aPSO) integrated with proposed haDEPSO. In aDE a novel, mutation, crossover and selection strategy is introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also in haDEPSO individual population is merged with other in a predefined manner, to balance between global and local search capability. The performance of proposed haDEPSO and its component aDE and aPSO are validated on 23 unconstrained benchmark functions, then solved five small (structural engineering) and one large (economic load dispatch)scale engineering design optimization problems. Outcome analyses confirm superiority of proposed algorithms over many stateoftheart algorithms.
Introduction
The success of any optimization algorithm majorly depends on its proficiency to solve the complex engineering design optimization problems. Most of the design optimization problems in engineering are turning out to be complicated due to involving mixed (discrete and continuous) variables under complex constraints. Generally, these problems are small and largescale nonlinear constrained problems and hence cannot be solved by traditional methods efficiently. Also, these problems can be represented as follows mathematically.
where \(\,\,\,f\,\,\,\): realvalued function, \(g_{l}\): inequality constraint and \(h_{k}\): equality constraint (these may be linear or nonlinear), \(x \in R^{D}\): \(D\)dimensional decision vector, \(l_{j}\) and \(u_{j}\): lower and upper limits for \(j\)th decision vector. \(L\) and \(K\): total number of inequality and equality constraints.
Many conventional optimization algorithms like Newton or quasiNewton have been developed to solve engineering design optimization problems. However, they have certain inherent drawbacks like high computational complexity, local optimal stagnation and derivation of the search space [1]. Also, it is difficult to find the optimal solution in the solving process. Presently, to overcome the drawbacks of conventional optimization methods, a bunch of optimization methods known as metaheuristics algorithms (MAs) has been introduced to solve complex engineering design optimization problems. According to the mechanical differences, the MAs can be categorized into four groups as follows: swarm intelligence algorithms (SIAs)—inspired from behavior of social insects or animals, evolutionary algorithms (EAs)—inspired from biology, physicsbased algorithms (PBAs)—inspired by the rules governing a natural phenomenon and human behaviorbased algorithms (HBAs)—inspired from the human being. Some instances of these algorithms are listed as follows.

(i)
Swarm intelligence algorithms (SIAs): PSO (Particle Swarm Optimization) [2], ABC (Artificial Bee Colony Algorithm) [3], GWO (Grey wolf optimizer HHO) [4], (Harris hawks optimization: Algorithm and applications) [5], CS (Cuckoo Search) [6], DA (Dragonfly Algorithm) [7], KH (Krill Herd) [8], etc.

(ii)
Evolutionary algorithms (EAs): DE (Differential Evolution) [9] and GA (Genetic Algorithm) [10] are representatives of EAs.

(iii)
Physicsbased algorithms (PBAs): GSA (Gravitational Search Algorithm) [11], EO (Equilibrium optimizer) [12], HS (Harmony Search) [13], WCA (Water Cycle Algorithm) [14], etc.

(iv)
Human behaviorbased algorithms (HBAs): TLBO (Teaching–learningbased optimization) [15], MBA (Mine blast algorithm) [16] and so on.
Among many MAs, DE and PSO have been widely used in continuous/discrete, constrained as well as constrained/unconstrained optimization problems. DE has remarkable performance and become a powerful optimizer in the field of realworld problems. However, it has few issues such as convergence rate and local exploitation ability. In order to overcome its shortcomings, lots of DE has been designed in the literature like JADE [17], SADE [18], CDE [19], modified DE [20] and DSSMDE [21]. Also, PSO has attracted attention to solve many complex optimization problems due to its efficient search ability and simplicity. However, main drawback of PSO is that it may easily get stuck at a local optimal solution region. Therefore, accelerating convergence speed and avoiding local optimal solutions are two critical issues in PSO. To overcome such issues many modified PSO are proposed in the literature such as HEPSO [22], RPSOLF [23], CPSO [24], IPSO [25], QPSO [26], PSOOPS [27], MTVPSO [28], IPSO [29], MPSOTVAC [30], IPSOTVAC [31], \(\theta\)PSO [32] and MPSO [33]. Furthermore, hybrid strategy is one of the main research directions to improve the performance of single algorithm. Therefore, to enhance the performance of DE and PSO, lots of their hybrids are presented in the literature like FAPSO [34], PSOSCALF [35], CSDE [36], PSOSCANMS [37], DEPSO [38] and DPD [39].
Although a large number of MAs are introduced in the literature, they could not able to solve variety of problems [40]. In other words, a method may have the acceptable results for some problems, but not for others. Thus, there is a need to introduce some effective algorithms to solve a wider range of optimization problems. Also, hybrid techniques are now more favored over their individual effort. Hence, it is the motivation of this study to present novel variants of DE and PSO with their hybridization.
Moreover, after an extensive literature review on different variants of DE and PSO with their hybridization, following points are analyzed and motivated from them.

(i)
In DE mutation and crossover strategy with their associate control parameters utilized to produce global best solution and beneficial for improving convergence behavior. Therefore, appropriate strategies and their associated parameter values of DE are considered a vital research study.

(ii)
The performance of PSO greatly depends on its parameters like acceleration coefficients (guide particles to the optimum) and inertia weight (balancing diversity). Hence, many researchers have tried to modify control parameter of PSO to achieve better accuracy and higher speed.

(iii)
Hybrid algorithms have aroused interest of researchers due to its effectiveness for complex optimization problems. Since DE and PSO have complementary properties, therefore their hybrids has gained prominence recently. To best of our knowledge, finding ways to combine DE and PSO is still an open problem.
Motivated by above observations and literature survey, following major contributions have been outlined for solving small and largescale engineering design optimization problems. Smallscale engineering design optimization problems include welded beam design (WBD), threebar truss design (TRD), pressure vessel design (PVD), speed reducer design (SRD) and tension/compression spring design (T/CSD), whereas in largescale engineering design optimization problem namely economic load dispatch (ELD) with or without valvepoint effects considering 3, 6, 15, 40 and 140unit test system.

(i)
Developed an advanced differential evolution (aDE) where combination of novel strategies with their associated parameter values are familiarized.

(ii)
Suggested an advanced particle swarm optimization (aPSO) which consists of novel gradually varying (decreasing and/or increasing) parameters.

(iii)
Designed an advanced hybrid algorithm by hybridizing advanced DE and PSO (haDEPSO: hybridization of aDE and aPSO).
Methods
In this section, following proposed methodology has been described in detail: (i) advanced differential evolution (aDE), (ii) advanced particle swarm optimization (aPSO) and (iii) hybrid haDEPSO.
Advanced differential evolution (aDE)
In suggested advanced DE (aDE), modified mutation strategy and crossover rate as well as changed selection scheme are introduced as follows.
Mutation:
where \(x_{i,j}^{t}\): target vector, \(v_{i,j}^{t}\): mutant vector, \({ }rand\left( {0, 1} \right)\): uniformly spread random number between 0 and 1, \(best_{j}\): best vector and \(\tau\): convergence factor (elects searching scale of all vectors). The dynamic adjustments of convergence factor (\(\tau\)) are given as follows. (i) If \(\tau\) = 1, then a vector will be randomly generated in the range [\(x_{i,j}^{t} , best_{j}\)]. This can improve convergence rate of DE, but it may take risk of increasing probability of encountering local optima and (ii) if \(\tau = \mu \left( {1  t/t_{max} } \right) + 1\), where \(t\) and \(t_{max}\): current and total iteration, \(\mu\): positive constant (determining the maximal searching scale of all vectors). In \(1^{{{\text{st}}}}\) iteration, \(\tau\) ≈ \(\mu + 1\) (as \(t\) = 1 is much smaller than \(t_{max}\), then term \(t/t_{max}\) can be ignored). In \(max\) iteration, \(\tau\) = 1 (as \(\left( {1  t/t_{max} } \right) = 0\)). Therefore, \(\tau\) decreases linearly from \(\mu + 1\) to \(1\) during the whole optimization process. This can improve the convergence as well as avoid local optima.
Since \(\tau\) is composed of a series of large values and enlarges the exploring scale of all vectors earlier, whereas, \(\tau\) composed a series of large values earlier and small values later which ensure global and local search capacity respectively. Also, it guaranteed to exploring the search space for all vectors of the proposed mutation strategy. These ensure global and local search capacity as well as exploring search space of all vectors of the proposed mutation strategy.
Crossover:
In order to keep the global searching ability and improve convergence speed \(C_{r}\) is set as \(e\frac{{\left( {t  t_{max} } \right)}}{{t_{max} }}\). It guarantees of individual diversity in early stage which improves global search ability. Further reduce degree of difference among individuals which accelerate convergence rate in later stage.
Selection:
It emphasizes on the random nature of aDE which is formulated as follows.
where f (·): fitness function values and \(p\): random value in (0, 1]. In this selection each pioneer vector gets chance to survive and share its observed information with other vectors in the next steps. It implies searching capabilities are more enriched and advantageous for stabilizing essential exploration and exploitation trends to aDE. The pseudocode of the proposed aDE is presented below.
Advanced particle swarm optimization (aPSO)
Preferably, PSO needs strong exploration ability and exploitation capability at early and later phase of the evolution, respectively. In velocity update equation of PSO, inertia weight (\(w\)) and acceleration coefficient \((c_{1 } {\text{and }}c_{2} )\) are important factors to satisfy the above requirement with following concept.

(i)
large and small values of \(w\) assist exploration and exploitation, respectively.

(ii)
\(c_{1 }\) and \(c_{2}\) values facilitate exploitation and exploration of the search area based on ensuing strategies.
Considering all concerns like advantages, disadvantages and parameter influences of PSO, an advanced PSO (aPSO) is introduced in this study. It relies on novel gradually varying (decreasing and/or increasing) parameters (\(w, c_{1 } {\text{and }}c_{2}\)) stated as follows
where \(w_{i}\) and \(w_{f}\): initial and final values of \(w\); \(c_{1i}\) and \(c_{1f}\): initial and final values of \(c_{1 }\); \(c_{2i}\) and \(c_{2f}\): initial and final values of \(c_{2 }\); \(t\) and \(t_{max}\): iteration index and maximum number of iteration. Hence, velocity and position of the \(i\)th particle are updated by following equations in the proposed aPSO. The pseudocode of proposed aPSO is presented below.
Hybrid advanced DEPSO (haDEPSO)
An advanced hybrid algorithm (haDEPSO) is proposed to further improve solution quality. In haDEPSO, entire population is sorted according to fitness function value and divided into two subpopulations, i.e.,\(pop_{{{ }1}}\) (best half) and \(pop_{{{ }2}}\) (rest half), since \(pop_{{{ }1}}\) and \(pop_{{{ }2}}\) contain best and rest half of the main population which implies good global and local search capability, respectively. In order to maintain local and global search capability, applying the proposed aDE (due to its good local search ability) and aPSO (because of its virtuous global search capability) on the respective subpopulation (\(pop_{{{ }1}}\) and \(pop_{{{ }2}}\)). Evaluating both subpopulations and then better solution obtained in \(pop_{{{ }1}}\) (by using aDE) and \(pop_{{{ }2}}\) (by using aPSO) are named as best and gbest separately. If best is less than gbest, then \(pop_{{{ }2}}\) is merged with \(pop_{{{ }1}}\) thereafter merged population evaluated by aDE (as it mitigate the potential stagnation). Otherwise, \(pop_{{{ }1}}\) is merged with \(pop_{{{ }2}}\) afterward merged population evaluated by aPSO (as it established to guide better movements). Basically, haDEPSO is based on relating superior capability of suggested aDE and aPSO. The flowchart of haDEPSO is demonstrated in Fig. 1 and the pseudocode described below.
Implementation of haDEPSO for optimization
The stepwise implementation of the proposed haDEPSO for Sphere function (continuous, convex and unimodal) is demonstrated in this section. The initial parameters for the considered function are given as follows.

(i)
Mathematical formulation: \({\text{Minimize}} f\left( x \right) = \mathop \sum \nolimits_{i = 1}^{D} x_{i}^{2}\)

(ii)
Range of decision variables: − 5 ≤ \(x_{i }\) ≤ 5

(iii)
Number of decision variables = 2

(iv)
Population size (\(np)\): 10

(v)
Number of iteration (\(t)\): 10
Stepwise execution of the proposed haDEPSO for Sphere function
for iteration t = 1.
Step I:
Initialization.
Generate a random initial population and evaluate the corresponding objective function value
required parameter:

for aDE \(\tau = \mu \left( {1  t/t_{max} } \right) + 1 = 0.01\left( {1  1/10} \right) + 1 = 1.009\), \(best_{j} = \left[ {  1.0178\; 0.1578} \right]\), \(C_{r} = 0.4065\) and \(p \in rand\left( {0,{ }1} \right]\).

for aPSO \(w = 0.8995\), \(c_{1} = 2.4600\), \(c_{2} = 0.508\), \(g_{best j}^{1} = \left[ {  4.7351\; 0.1238} \right]\) and initial velocity (\(v_{i,j}^{t}\)) generated randomly in between 0 and 1 is
$$v_{i,j}^{t} = \left[ {\begin{array}{*{20}c} {0.2394} & {0.2218} \\ {0.1908} & {0.2419} \\ {0.2818} & {0.5319} \\ {0.3976} & {0.4134} \\ {0.1934} & {0.2357} \\ \end{array} } \right]$$
Step II:
Sorting.
Arranging the population according to the fitness function values and dividing into two groups as \(pop_{1}\) and \(pop_{2}.\)
Step III:
Applying.
aDE in \({pop}_{ 1}\) and aPSO in \({pop}_{ 2}.\)
Advanced differential evolution (aDE) for \({{\varvec{p}}{\varvec{o}}{\varvec{p}}}_{1}\)
Best function value obtained by aDE is 1.0608 named as best on \(pop_{{{ }1}}\).
Advanced particle swarm optimization (aPSO) for \(pop_{{{ }2}}\)
Updated velocity \(v_{i,j}^{t}\) \(\left( {v_{i,j}^{t} = w v_{i,j}^{t} \left( {{\text{old}}} \right) + c_{1} r_{1} \left( {p_{best i,j}^{t}  x_{i,j}^{t} } \right) + c_{2} r_{2} \left( {g_{best j}^{t}  x_{i,j}^{t} } \right)} \right)\) and updated position \(x_{i,j}^{t}\) \(\left( {x_{i,j}^{t} = x_{i,j}^{t} \left( {{\text{old}}} \right) + v_{i,j}^{t} \left( {{\text{updated}}} \right)} \right)\) of aPSO are given as follows.
Best function value obtained by aPSO is 12.6788 named as gbest on \(pop_{{{ }2}}\).
Step IV:
Condition
best < gbest then merging \(pop_{{{ }2}}\) with \(pop_{{{ }1}}\) and applying again aDE else merging \(pop_{{{ }1}}\) with \(pop_{{{ }2}}\) then applying aPSO (best < gbest condition is applicable for iteration \(t\) = 1).
Mutation  Crossover  Selection 
\(v_{i,j}^{t} = \left[ {\begin{array}{*{20}c} {  1.0178} & {0.1578} \\ {0.0450} & {  0.5508} \\ {1.6195} & {0.4812} \\ {  1.7615} & {  0.5028} \\ {  1.4752} & {1.4133} \\ {2.7687} & {0.2405} \\ {0.6980} & {1.3878} \\ {  2.4487} & {  0.9516} \\ {  2.0096} & {  1.5468} \\ {  2.7895} & {2.1062} \\ \end{array} } \right]\)  \(u_{i,j}^{t} = \left[ {\begin{array}{*{20}c} {  1.0178} & {0.1578} \\ {0.0450} & {  0.5508} \\ {1.6195} & {0.4812} \\ {  1.7615} & {  0.5028} \\ {  1.4752} & {1.4133} \\ {  2.5053} & {0.2405} \\ {0.6980} & {2.6178} \\ {  2.4487} & {  0.9516} \\ {  2.0096} & {  1.5468} \\ {  4.5612} & {2.1062} \\ \end{array} } \right] = \left[ {\begin{array}{*{20}c} {1.0608} \\ {0.3054} \\ {2.8543} \\ {3.3556} \\ {4.1736} \\ {6.3343} \\ {7.3400} \\ {6.9016} \\ {6.4310} \\ {25.2406} \\ \end{array} } \right]\)  \(x_{i,j}^{t} = \left[ {\begin{array}{*{20}c} {  1.0178} & {0.1578} \\ {0.0450} & {  0.5508} \\ {1.6195} & {0.4812} \\ {  1.7615} & {  0.5028} \\ {  1.4752} & {1.4133} \\ {2.5053} & {0.2405} \\ {0.6980} & {2.6178} \\ {  2.4487} & {  0.9516} \\ {  2.0096} & {  1.5468} \\ {  4.5612} & {2.1062} \\ \end{array} } \right]\) 
Step V:
Termination criterion
Stop if optimal solution obtained otherwise repeat go to step II.
Similarly  for iteration t = 5  for iteration t = 10 
\(\left[ {\begin{array}{*{20}c} {  0.5984} & {0.1001} \\ {1.2541} & {  0.1241} \\ {1.7845} & {1.0012} \\ {  0.4512} & {  1.5231} \\ {  1.3251} & {0.1241} \\ {  3.0025} & {1.0154} \\ {1.7740} & {3.0015} \\ {  2.9632} & {  0.0214} \\ {  1.1254} & {  1.7458} \\ {  3.4125} & {2.0325} \\ \end{array} } \right]\)  \(\left[ {\begin{array}{*{20}c} {  0.1254} & {0.1201} \\ {0.2514} & {  1.0215} \\ {1.1245} & {1.3251} \\ {  0.6584} & {  0.5125} \\ {  1.7584} & {0.2141} \\ {  2.1254} & {0.0100} \\ {2.1102} & {3.0102} \\ {  3.1251} & {  0.1542} \\ {  1.3695} & {  0.3251} \\ {  2.0321} & {1.1254} \\ \end{array} } \right]\) 
Results and discussion
The validation of the proposed algorithms with the solution of small and largescale engineering design optimization problems is included in this section.
Validation of proposed algorithms
The justification of suggested component aDE and aPSO with the proposed haDEPSO algorithm evaluated on 23 unconstrained benchmark functions (UBFs). The descriptions of UBFs are listed in Table 1.
Simulations were conducted on Intel (R) Core (TM) i52350 M CPU @ 2.30 GHz, RAM: 4.00 GB, Operating System: Window 10, Cfree Standard 4.0. After an extensive analysis, the parameters used in the proposed algorithms are finetuned and recommended as follows.\(w_{i}\) (initial inertia weight) = 0.4, \(c_{1i}\) (initial cognitive acceleration coefficient) = 0.5 and \(c_{2i}\) (initial social acceleration coefficient) = 2.5 and \(w_{f}\) (final inertia weight) = 0.9, \(c_{1f}\) (final cognitive acceleration coefficient) = 2.5 and \(c_{2f}\) (final social acceleration coefficient) = 0.5. Moreover, to handle constraints constrained optimization problems transfers to unconstrained one by adding penalty term into objective function. The bracket operator penalty [41] is picked in the present study due to its higher efficiency. And after many experiments finetuning value of R = \(1e^{03}\) is acclaimed to use in proposed algorithms. The overall best values in each table are highlighted with boldface letters of the corresponding algorithms.
The produced result by proposed algorithms on 23 unconstrained benchmark functions (UBFs) is compared with traditional algorithms (HHO [5] and EO [12]), DE variants (JADE [17] and SHADE [18]), PSO variants (HEPSO [22] and RPSOLF [23]) and hybrid variants (FAPSO [34] and PSOSCALF [35]). The parameters of all above compared and proposed algorithms are listed in Table 2. For fair comparison population size, stopping criteria and independent run of proposed algorithms is taken as minimum of corresponding comparative algorithms. The comparative experimental results in terms of mean, std. (standard deviation) and ranking of the objective function values are presented in Table 3 of 30 independent runs.
It should be noted that from Table 3, the mean objective function values of the proposed aDE, aPSO and haDEPSO algorithms are better and/or equal in comparison of abovelisted traditional algorithms, DE variants, PSO variants and hybrid variants. As per the experimental results shown in Table 3, the following comparison results are summarized as follows for UBFs cases (i). Unimodal function (f_{1}–f_{7}): proposed haDEPSO obtained better results in all functions (f_{1}–f_{6}) meanwhile slightly inferior on f_{7}, suggested aDE obtained better results for f_{1}, f_{2}, f_{3} and f_{6} functions whereas aPSO achieve better results for f_{1} and f_{2} as well as marginally similar for the rest functions. (ii). Multimodal function (f_{8}–f_{13}): proposed haDEPSO obtained better results for all six functions (f_{8}–f_{13}) and similar for f_{8} (on JADE and PSOSCALF), f_{9} (on EO, RPSOLF, FAPSO and PSOSCALF), f_{11} (on EO, RPSOLF and PSOSCALF) and f_{13} (on PSOSCALF). Suggested aDE attained better results for f_{8}, f_{9}, f_{11} and marginally similar/inferior for the rest functions, whereas aPSO obtained better result for f_{9} and slightly inferior for the rest. (iii). Fixeddimension function (f_{14}–f_{23}): proposed haDEPSO and aDE exhibits best performance on all functions, meanwhile aPSO obtained marginally better or equal results compared to other algorithms.
Moreover, all algorithms are individually ranked (as ‘1’ for the best and ‘2’ for subsequent performer and so on) in Table 3 based on mean result values. From this table it is concluded that haDEPSO, aDE and aPSO ranked 1st, 2nd and 4th sequentially. Also, average and overall rank of proposed algorithms Vs others is presented in Table 3. It is clear that (from ranking) performances of proposed algorithms are superior to others. Eventually, proposed aDE, aPSO and haDEPSO produce less std. (it may 0.00E+00) for most of the cases on UBFs which describe their stability. Furthermore, superiority of proposed algorithms is statistically validated over other algorithms through onetailed t test (with 98 degree of freedom (df) at 5% significance level) and Wilcoxon signed rank (WSR) test (at 5% significance level). The details of these tests can be found in [42]. The results of t test and WSR test on UBFs are reported in Table 4. From Table 4 it can be seen that proposed algorithms have both ‘a (significantly better than other)’ and ‘a^{+} (highly significance with other)’ sign (in case of t test) and perform better or equally (in case WSR test) in most of consequence. Also, the p values as reported in Table 4 of the proposed algorithms are less with others which conclude that simulations are reliable for the majority of runs.
The convergence speed of proposed and comparative algorithms is compared over 8 (f_{1}, f_{5}, f_{6}, f_{7}, f_{8}, f_{9}, f_{10} and f_{11}) typical 30D UBFs. All plotted convergence graphs (objective function values Vs iterations) are separately presented in Fig. 2a–h. From these figures, it can be concluded that proposed aDE, aPSO and haDEPSO converge much faster than other algorithms in all cases.
Also, an attempt is made to find global optimal solution total of 690 runs (30 runs for each UBFs with 30 population size) and illustrated in Fig. 3. It confers that the proposed algorithms score the highest optimum solutions.
Apart from this, computational time of proposed and compared algorithms on each UBF is computed and presented through box plots in Fig. 4. From this figure, it can be perceived that the proposed algorithms take lesser time to achieve the best value for the entire UBFs.
As a whole, above numerical, statistical and graphical result analysis shows that proposed aDE, aPSO and haDEPSO are performed very competitive and/or equally with other compared algorithms. However, among three proposed algorithms haDEPSO is superior.
Application
In order to further examine proposed algorithms aDE, aPSO and haDEPSO are further applied to solvefollowing five wellknown small and one largescale engineering design optimization problem.
Smallscale engineering design optimization problems
Considered smallscale engineering design optimization problems are briefed as follows.

(i)
Welded beam design (WBD) problem
Its objective is to find the minimum cost design of a structural welded beam design, subject to constraints \(g_{1}\): shear stress \(\left( \tau \right)\), \(g_{2}\): bending stress in the beam (\(\sigma\)), \(g_{3}\), \(g_{4}\), \(g_{5}\): side constraints, \(g_{6}\): end deflection of the beam \(\left( \delta \right)\) and \(g_{7}\)_{:} bucking load on the bar (\(P_{c}\)) with four design variables \(h\left( {x_{1} } \right)\): thickness of the weld, \(l\left( {x_{2} } \right)\): length of the welded joint, \(t\left( {x_{3} } \right)\): width of the beam and \(b\left( {x_{4} } \right)\): thickness of the beam. The schematic diagram of this problem is presented in Fig. 5, and it can be formulated as follows.
where \(\tau \left( x \right) = \sqrt {\left( {\dot{\tau }} \right)^{2} + 2\dot{\tau }\ddot{\tau }\frac{{x_{2} }}{2R} + \left( {\ddot{\tau }} \right)^{2} }\), \(\dot{\tau } = \frac{p}{{\sqrt 2 x_{1} x_{2} }}\), \(\ddot{\tau } = \frac{MR}{J}\), \(M = P\left( {L + \frac{{x_{2} }}{2}} \right)\), \(R = \sqrt {\frac{{x_{2}^{2} }}{4} + \left( {\frac{{x_{1} + x_{3} }}{2}} \right)^{2} }\), \(J = 2\left\{ {\sqrt 2 x_{1} x_{2} \left[ {\frac{{x_{2}^{2} }}{12} + \left( {\frac{{x_{1} + x_{3} }}{2}} \right)^{2} } \right]} \right\}\), \(\sigma (x) = \frac{{6PL}}{{x_{4} x_{3}^{2} }},\delta (x) = \frac{{4PL^{3} }}{{Ex_{3}^{3} x_{4} }},P_{c} (x) = \frac{{4.013E\sqrt {\left( {x_{3}^{2} x_{4}^{6} /36} \right)} }}{{L^{2} }} \times \left( {1  \frac{{x_{3} }}{{2L}}} \right)\sqrt {\frac{E}{{4G}}}\).
\(P = 61lb, L = 14{\text{in}},{\text{ E}} = 30 \times 10^{6} {\text{psi}},{ }G = 12 \times 10^{6} {\text{psi}},\, \tau_{max} = 13,600{\text{psi}},{ }\sigma_{max} = 30,600{\text{psi}}, \delta_{max} = 0.25{\text{in}}.\)

(ii)
Threebar truss design (TRD) problem
It is dealt with the design of a threebar truss structure in which the volume is to be minimized subject to stress constraints. The problem has two decision variables and three constraints. The schematic diagram of this problem is presented in Fig. 6 and it can be formulated as follows.
where \(0 \le x_{i} \le 1, i = 1,2;l = 100\,{\text{cm}}, P = 2\,{\text{kN/cm}}^{2},\;\sigma = 2\,{\text{kN/cm}}^{2}.\)

(iii)
Pressure vessel design (PVD) problem
Its objective is to minimize the total cost \(f\left( x \right)\), including cost of the material, forming and welding with variables Ts (thickness of the shell), T_{h} (thickness of the head), R (inner radius) and L (length of the cylindrical section of the vessel). Both thickness variables (Ts, Th) must be integer multiple values of 0.0625 inch, which is the available thickness of rolled steel plates. R and L are continuous variables. A cylindrical vessel is capped at both ends by hemispherical heads. The schematic diagram of this problem is presented in Fig. 7 and it can be formulated as follows.
where \(0 \le x_{i} \le 100;i = 1,2 \;{\text{and}}\;10 \le x_{i} \le 200;i = 3, 4.\)

(iv)
Speed reducer design (SRD) problem
Its aim is to minimize the weights of the speed reducer subject to constraints on bending stress of the gear teeth, surface stress, transverse deflections of the shafts and stresses in the shafts. The variables \(x_{1}\) to \(x_{7}\) represent the face width (b), module of teeth (m), number of teeth in the pinion (z), length of the first shaft between bearings (\(l_{1}\)), length of the second shaft between bearings (\(l_{2}\)) and the diameter of first (\(d_{1}\)) and second shafts (\(d_{2}\)), respectively. This is an example of a mixed integer programming problem. The third variable \(x_{3}\) (number of teeth) is of integer values while all rest variables are continuous type. The schematic diagram of this problem is presented in Fig. 8 and it can be formulated as follows.
where \(2.6 \le x_{1} \le 3.6 , 0.7 \le x_{2} \le 0.8, 17 \le x_{3} \le 28, 7.3 \le x_{4} \le 8.3, 7.3 \le x_{5} 8.3\), \(2.9 \le x_{6} \le 3.9, 5.0 \le x_{7} \le 5.5.\)

(v)
Tension/compression spring design (T/CSD) problem
It minimizes the weight of the tension/compression spring, subject to constraints on the minimum deflection, shear stress, surge frequency, limits on outside diameter and on design variables. The design variables are wire diameter \(d\left( {x_{1} } \right)\), mean coil diameter \(D\left( {x_{2} } \right)\) and number of active coils \(P\left( {x_{3} } \right)\). The schematic diagram of this problem is presented in Fig. 9, and it can be formulated as follows.
where \(0.05 \le x_{l} \le 2.00, 0.25 \le x_{2} \le 1.30\),\(2.00 \le x_{3} \le 15.00 .\)
The results of the proposed hybrid haDEPSO and its suggested component algorithms aDE and aPSO algorithm on five smallscale engineering design optimization problems are compared with PSO [2], ABC [3], GWO [4], CS [6], DA [7], KH [8], DE [9], GA [10], GSA [11], EO [12], HS [13], WCA [14], MBA [16], CDE [19], modified DE [20], DSSMDE [21], CPSO [24], IPSO [25], QPSO [26], PSOOPS [27], CSDE [36], PSOSCANMS [37], ACO [43], CSKH [44], SCA [45], SBM [46], FSA [47], EPO [48], MVO [49], SHO [50], AFA [51], SAC [52] and GSAGA [53]. For fair comparison population size (30), stopping criteria (1500 iterations) and independent run (25) of proposed algorithms are taken same as comparative algorithms. The results of the comparative algorithms are taken from the original references, rest parameter of proposed algorithms as same as above. The optimal and comparative results of proposed algorithms with others on respective smallscale engineering design optimization problems are presented in Tables 5 and 6 (for WBD), Tables 7 and 8 (for TRD), Tables 9 and 10 (for PVD), Tables 11 and 12 (for SRD) and Tables 13 and 14 (for T/CSD).
As delineated in these tables, the produced optimal cost by proposed algorithms are summarized as follows for all five problems: (i) proposed aDE for WBD, TRD, PVD, SRD and T/CSD are 1.70541, 261.2654, 5885.3279, 2992.1242 and 0.012552, respectively, (ii) proposed aPSO for WBD, TRD, PVD, SRD and T/CSD are 1.70845, 262.8536, 5885.3079, 2994.2442 and 0.012568, respectively, and (iii) proposed haDEPSO for WBD, TRD, PVD, SRD and T/CSD are 1.69782, 261.1438, 5882.4387, 2990.3582 and 0.012475, respectively. Further, it can be concluded that the proposed algorithm outperformed and achieves the result with better best, worst, mean than other comparative algorithms. Moreover, securing less std. produced by proposed aDE, aPSO and haDEPSO in all five problems describe their stability. Therefore, the proposed algorithm shows superior and competitive performance to other algorithms in all considered smallscale engineering problems.
The convergence graphs of all proposed and best nonproposed algorithm (to avoid complicacy) is plotted for all smallscale engineering design optimization problems and presented in Fig. 10a–e. From these figures, it can be clearly visualized that proposed algorithms converge faster than others. Hence, proposed algorithms are computationally efficient.
In general, from the all above result analysis it can be declared that proposed aDE, aPSO and haDEPSO are performing better and/or equally with others. However, among three proposed algorithms haDEPSO has larger competence.
Largescale engineering design optimization problem: Economic load dispatch (ELD) problem with or without valvepoint effects
Objective function of ELD problem with succeeding constraints can be represented as follows.
where \(F\): total fuel cost, \(n\): number of generating unit, \(F_{i} \left( {P_{i} } \right)\): operating fuel cost (real power output) and \(a_{i} ,b_{i } \& c_{i}\): cost coefficient of generating unit \(i\). And \(P_{i}^{min}\): minimum generation limit of unit i.
Constraints.

Generator constraint: \(P_{i}^{{{\text{min}}}} \le P_{i} \le P_{i}^{{{\text{max}}}}\), where \(P_{i}^{{{\text{min}}}}\) and \(P_{i}^{{{\text{max}}}}\): minimum and maximum power generation by unit \(i\).

Power balance constraint: \(\mathop \sum \nolimits_{i = 1}^{n} P_{i} = D + P_{L}\), with \(P_{L} = \mathop \sum \nolimits_{i = 1}^{n} \mathop \sum \nolimits_{j = 1}^{n} P_{i} B_{ij} P_{j} + \mathop \sum \nolimits_{i = 1}^{n} P_{i} B_{oi} + B_{oo}\), where \(D\): total load demand, \(P_{L}\): total transmission line loss and \(B_{ij}\),\(B_{oi}\),\(B_{oo}\): transmission loss coefficient.

Prohibited operating zone constraint: \(P_{i}^{{{\text{min}}}} { } \le P_{i} \le P_{i,1}^{l}\): \(P_{i,k  1}^{u} \le P_{i} \le P_{i,k}^{l}\): \(P_{{i,n_{i} }}^{u} \le P_{i} \le P_{i}^{{{\text{max}}}}\) ; \(k = 2,3, \ldots n_{i}\) where \(n_{i}\): number of prohibited operating zone and \(P_{i,k}^{l}\) and \(P_{i,k}^{u}\): lower and upper limit of \(k\)th prohibited zone of generating unit \(i.\)

Ramp rate limit constraint: \({\text{max}}\left( {P_{i}^{{{\text{min}}}} ,P_{i}^{t  1}  {\text{DR}}_{i} } \right) \le P_{i}^{t} \le {\text{min}}\left( {P_{i}^{{{\text{max}}}} { },P_{i}^{t  1} + {\text{UR}}_{i} } \right)\), where \(P_{i}^{t}\) and \(P_{i}^{t  1}\) current and previous output power and \(UR_{i}\) and \(DR_{i}\): up and down ramp limit of generating unit \(i\).
In the next section, ELD problem is solved with and without valvepoint loading effects using below considered 3, 6, 15, 40 and 140unit test system (TSys) and the results are compared stateoftheart algorithms.
Test systems  Description 

TSys1 (3unit test system) [54]  It involves valvepoint effects with 850 MW total demand 
TSys2 (6unit test system) [55]  It consists of transmission losses, ramprate limit and prohibited operating zone constraints with 1263 MW total demand 
TSys3 (15unit test system) [55]  It comprises ramprate limits and prohibited operating zone constraints with 2630 MW total demand 
TSys4 (40unit test system) [54]  It implicates valvepoint effects with 10,500 MW total demand 
TSys5 (140unit test system) [56]  It consists of ramprate limits, valvepoint loading effects and prohibited operating zone constraints with 49,342 MW total demand 
The results produced by proposed algorithms on above considered different test systems of ELD problem are compared with other stateoftheart algorithms. These compared algorithms are listed as follows: PSO [2], DE [9], GA [10], MTVPSO [28], IPSO [29], MPSOTVAC [30], IPSOTVAC [31], θPSO [32], MPSO [33], DEPSO [38], DPD [39], THS [57], MGSO [58], EHM [59], BCO [60], NCS [61], IABC [62] and DHS [63]. In order to check the efficiency of the proposed algorithms aDE, aPSO and haDEPSO, least values of population size (30), maximum number of iterations (1000) and independent runs (30) have been considered among compared algorithms.
The comparative simulation results of proposed and compared algorithms for TSys1, TSys2, TSys3, TSys4 and TSys5 are reported in Tables 15, 16, 17, 18 and 19, respectively, over 30 runs.
As reported in these tables, the global optimal cost produced by (i) proposed aDE for TSys1, TSys2, TSys3, TSys4 and TSys5 are 8234.0719 ($/h), 15,441.3561 ($/h), 32,542.7820 ($/h), 121,405.7384 ($/h) and 1,560,436.76 ($/h), respectively, (ii) proposed aPSO for TSys1, TSys2, TSys3, TSys4 and TSys5 are 8234.0721 ($/h), 15,441.8451 ($/h), 32,542.4512 ($/h), 121,404.5378 ($/h) and 1,560,435.88 ($/h), respectively, and (iii) proposed haDEPSO for TSys1, TSys2, TSys3, TSys4 and TSys5 are 8234.0717 ($/h), 15,440.1288 ($/h), 32,542.1452 ($/h), 121,403.5454 ($/h) and 1,560,434.54 ($/h), respectively. These reported cost results for all test systems show that the proposed algorithms succeed in finding the best solution in comparison with other algorithms. Furthermore, the mean and maximum fuel cost together with standard deviation and CPU mean time for each test systems are also recorded in the same tables. Also, it can be seen from these tables that proposed algorithms surpassed all other comparative algorithms by providing the best result with regard to minimum, mean and maximum cost. It is noteworthy that the proposed algorithms can still yield better solutions with low standard deviations and acceptable CPU time. It signifies that the proposed algorithm has stronger convergence with higher stability and reliability/robustness compared to other existing algorithms.
The convergence curves of proposed with compared algorithms are plotted in Fig. 11a–e for TSys1, TSys2, TSys3, TSys4 and TSys5 of ELD problem in terms of fuel cost and iterations. This figure shows that proposed algorithms aDE, aPSO and haDEPSO have more robust convergence where the results improved as the iterations increased.
Conclusion
In this study, an advanced hybrid algorithm haDEPSO proposed for solving small and largescale engineering design optimization problems, where an advanced DE (aDE) and PSO (aPSO) are integrating in suggested hybrid. The brief summary of these proposed algorithms is given as follows.

(i)
An advanced hybrid algorithm (haDEPSO) has been established by combining aDE and aPSO. It is based on multiswarm approach where the population of one is merged with other in a predefined manner which yields guaranteed convergence and diversifying the solutions.

(ii)
To enhance performance and easily adjust the control parameters of DE, an advanced DE (aDE) is developed. The novel mutation strategy, crossover probability and altered selection schemes of aDE will guarantee high and low population diversity at start and end of the algorithm, respectively.

(iii)
To avoid particles stagnant, an advanced PSO (aPSO) is proposed which consists of novel gradually varying (decreasing and/or increasing) parameters. These control parameters can wellbalance the exploration and exploitation capabilities and promotes the particles to search highquality solution of aPSO.
The effectiveness of the proposed hybrid haDEPSO and its suggested component algorithms aDE and aPSO algorithm are tested on 23 unconstrained benchmark functions, then applied on five wellknown small engineering design optimization problems, namely welded beam design (WBD), threebar truss design (TRD), pressure vessel design (PVD), speed reducer design (SRD) and tension/compression spring design (T/CSD) problem and one largescale engineering design optimization problem, viz., economic load dispatch (ELD) having five different test systems (3, 6, 15, 40, 140unit). The numerical, statistical and graphical analyses of the proposed algorithms are compared against the stateoftheart algorithms. The comparative results shape that the proposed algorithms become more robust and effective to solve complex engineering design optimization problems. Thus, it is conclusive that the proposed algorithms can be treated as a vital alterative in the field of MAs. Moreover, in the view of feasibilities, superiorities and solution optimality, among all and suggested algorithms haDEPSO outperformed.
In addition, the proposed algorithms have higher time complexity compared to some DE, PSO and hybrid variants. The main reason of timeconsuming of proposed algorithms is the matrix operation execution. This operation is repeated per individuals in each iteration and increases the running time of the algorithm to some extent. Moreover, the proposed algorithms may not suitable for all engineering design optimization problems as others. As a part of our future work, some novel parameters will be designed for the proposed aDE, aPSO and haDEPSO in the hope of finding more accurate solutions and reduce the time complexity. Additionally, effectiveness of the proposed algorithms can be tested by some more complicated realworld applications and new MAs will be developed in future. Finally, this paper is expected more attention to the analysis of how to strengthen the robustness of the proposed algorithms for complex optimization problems.
Availability of data and materials
We confirm that all data generated or analyzed during this study are included in the submitted manuscript. All authors confirm that all relevant data are included in the article in the “Application” section and “Conclusion” section from Table 1 to Table 19. Also, availability of data and materials are cited in references of the manuscript. Moreover, the data that support the findings of this study are available from the corresponding author upon reasonable request. No additional data archiving is necessary.
Abbreviations
 MAs:

Metaheuristics algorithms
 SIAs:

Swarm intelligence algorithms
 EAs:

Evolutionary algorithms
 PBAs:

Physicsbased algorithms
 HBAs :

Human behaviorbased algorithms
 PSO:

Particle swarm optimization
 ABC:

Artificial bee colony
 GWO:

Grey wolf optimizer
 HHO:

Harris hawks optimization
 CS:

Cuckoo search
 DA:

Dragonfly algorithm
 \(\,\,\,f\,\,\,\) :

Realvalued function
 \(D\) :

Dimension
 \(l_{j}\) and \(u_{j}\) :

Lower and upper limits for jth decision vector limits
 \(L\) and \(K\) :

Total number of inequality and equality constraint
 \(g_{l}\) :

Inequality constraint
 \(h_{k}\) :

Equality constraint
 \(x_{i}\) :

Position vector of ith particle
 \(v_{i}\) :

Velocity vector of ith particle
 \(pbest_{i }\) :

Individual best position of ith particle
 KH:

Krill Herd
 DE:

Differential evolution
 GA:

Genetic algorithm
 GSA:

Gravitational search algorithm
 EO:

Equilibrium optimizer
 HS:

Harmony search
 WCA:

Water cycle algorithm
 TLBO:

Teaching–learningbased optimization
 MBA:

Mine blast algorithm
 UBFs:

Unconstrained benchmark functions
 WBD:

Welded beam design
 \(gbest_{j }\) :

Global best position of particle
 \(t\) :

Iteration index
 \(c_{1}\) and \(c_{2}\) :

Cognitive and social acceleration coefficient
 \(r_{1}\) and \(r_{2}\) :

Uniform random numbers in [0, 1]
 \(w\) :

Inertia weight
 \(np\) :

Population size
 \(x_{l}\) and \(x_{u}\) :

Lower and upper boundaries
 \(x_{ij}^{t}\) :

Target vector
 \(v_{ij }^{t}\) :

Mutant vector
 SRD:

Speed reducer design
 TRD:

Threebar truss design
 PVD:

Pressure vessel design
 T/CSD:

Tension/compression spring design
 ELD:

Economic load dispatch
 aDE:

Advanced differential evolution
 aPSO:

Advanced particle swarm optimization
 haDEPSO:

Hybrid Advanced DEPSO
 std.:

Standard deviation
 df :

Degree of freedom
 WSR:

Wilcoxon signed rank
 \(u_{ ij}^{t}\) :

Trial vector
 \(F\) :

Scaling vector
 \(C_{r}\) :

Crossover rate
 i :

\(\in \left[ {1,np} \right]\)
 j :

\(\in \left[ {1,D} \right]\)
 rand :

Random numbers
 t _{ max } :

Maximum iterations
 \(\tau\) :

Convergence factor
References
Simpson AR, Dandy GC, Murphy LJ (1994) Genetic algorithms compared to other techniques for pipe optimization. J Water Resour Plan Manag 20:423–443
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceeding of IEEE international conference on neural networks, pp 1942–1948
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony algorithm. J Glob Optim 39(3):459–471
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872
Yang XS, Deb S (2009) Cuckoo Search via Lévy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing, Coimbatore, India, pp 210–214
Mirjalili S (2016) Dragonfly algorithm: a new metaheuristic optimization technique for solving singleobjective, discrete and multiobjective problems. Neural Comput Appl 27(4):1053–1073
Gandomi AH, Alavi AH (2012) Krill herd: a new bioinspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359
Davis L (1991) Handbook of genetic algorithms
Rashedi E, Nezamabadipour H, Saryazdi S (2009) A gravitational search algorithm. Inf Sci 179(13):2232–2248
Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2019) Equilibrium optimizer: a novel optimization algorithm. KnowlBased Syst 191:1–34
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. SIMULATION 76(2):60–68
Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166
Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning—based optimization: an optimization method for continuous nonlinear large scale problems. Inf Sci 183:1–15
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612
Zhang J, Sanderson C (2009) JADE: Adaptive Differential Evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958
Tanabe R, Fukunaga A (2013) Successhistory based parameter adaptation for Differential Evolution. In: IEEE Congress on Evolutionary Computation, pp 71–78
Huang F, Wang L, He Q (2007) An effective coevolutionary differential evolution for constrained optimization. Appl Math Comput 186:340–356
Montes EM, Coello C, Reyes J, MuñozDávila L (2007) Multiple trial vectors in differential evolution for engineering design. Eng Optim 39:567–589
Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178(15):3043–3074
Mahmoodabadi MJ, Mottaghi ZS, Bagheri A (2014) High exploration particle swarm optimization. J Inf Sci 273:101–111
Yan B, Zhao Z, Zhou Y, Yuan W, Li J, Wu J, Cheng D (2017) A particle swarm optimization algorithm with random learning mechanism and Levy flight for optimization of atomic clusters. Comput Phys Commun 219:79–86
He Q, Wang L (2007) An effective coevolutionary particle swarm optimization for constrained engineering design prob . Eng Appl Artif Intell 20:89–99
He S, Prempain E, Wu QH (2004) An improved particle swarm optimizer for mechanical design optimization problems. Eng Optim 36:585–605
Coelho L (2010) Gaussian quantumbehaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37:1676–1683
Isiet M, Gadala M (2020) Sensitivity analysis of control parameters in particle swarm optimization. J Comput Sci 41:1–33
Parouha RP (2019) Nonconvex/nonsmooth economic load dispatch using modified timevarying particle swarm optimization. Comput Intell 35:717–744. https://doi.org/10.1111/coin.12210
Safari A, Shayegui H (2011) Iteration particle swarm optimization procedure for economic load dispatch with generator constraints. Expert System Appl 38(5):6043–6048
Abdullah MN, Bakar AHA, Rahim NA, Mokhlis H, Illias HA, Jamian JJ (2014) Modified particle swarm optimization with time varying acceleration coefficients for economic load dispatch with generator constraints. J Electr Eng Technol 9(1):15–26
Mohammadi BL, Rabiee A, Soroudi A, Ehsan M (2012) Iteration PSO with time varying acceleration coefficients for solving nonconvex economic dispatch problems. Int J Electr Power Energy Syst 42(1):508–516
Hosseinnezhad V, Babaei E (2013) Economic load dispatch using θPSO. Int J Electr Power Energy Syst 49:160–169
Basu M (2015) Modified particle swarm optimization for nonconvex economic dispatch Problems. Electr Power Energy Syst 69:304–312
Xia X, Gui L, He G, Xie C, Wei B, Xing Y, Tang Y (2018) A hybrid optimizer based on firefly algorithm and particle swarm optimization algorithm. J Comput Sci 26:488–500
Chegini SN, Bagheri A, Najafi F (2018) A new hybrid PSO based on sine cosine algorithm and Levy flight for solving optimization problems. Appl Soft Comput 73:697–726
Zhang Z, Ding S, Jia W (2019) A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Eng Appl Artif Intell 85:254–268
Fakhouri HN, Hudaib A, Sleit A (2020) Hybrid particle swarm optimization with Sine Cosine Algorithm and NelderMead Simplex for solving engineering design problems. Arab J Sci Eng 4:3091–3109
Sayah S, Hamouda A (2013) A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems. Appl Soft Comput 13(4):1608–1619
Parouha RP, Das KN (2016) A novel hybrid optimizer for solving economic load dispatch problem. Int J Electr Power Energy Syst 78:108–126
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82
Deb K (1995) Optimization for engineering design: algorithms and examples. PrenticeHall of India, New Delhi
Das KN, Parouha RP (2015) An ideal tripopulation approach for unconstrained optimization and applications. Appl Math Comput 256:666–701
Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27:155–182
Basset M, Wang G, Sangaiah AK, Rushdy E (2019) Krill Herd algorithm based on cuckoo search for solving engineering optimization problems. Multimed Tools Appl 78:3861–3884
Mirjalili S (2016) SCA: A sine cosine algorithm for solving optimization problems. KnowlBased Syst 96:120–133
Akhtar S, Tai K, Ray T (2002) A sociobehavioural simulation model for engineering design optimization. Eng Optim 34:341–354
Hedar AR, Fukushima M (2006) Derivativefree filter simulated annealing method for constrained continuous global optimization. J Glob Optim 35:521–549
Dhiman G, Kumar V (2018) Emperor Penguin optimizer: a bioinspired algorithm for engineering problems. KnowlBased Syst 159:20–50
Mirjalili S, Mirjalili SM, Hatamlou A (2015) Multiverse optimizer: a natureinspired algorithm for global optimization. Neural Comput Appl 27(2):495–513
Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bioinspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70
Baykasoglu A, Ozsoydan FB (2015) Adaptive firefly algorithm with chaos for mechanical design optimization problems. Appl Soft Comput 36:152–164
Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput 7(4):386–396
Garg H (2019) A hybrid GSAGA algorithm for constrained optimization problems. Inf Sci 478:499–523
Sinha N, Chakrabarti R, Chattopadhyay PK (2003) Evolutionary programming techniques for economic load dispatch. IEEE Trans Evol Comput 7(1):83–94
Gaing ZL (2003) Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans Power Syst 18(3):1187–1195
dos Santos Coelho L, Bora TC, Mariani VC (2014) Differential evolution based on truncated Lévytype flights and population diversity measure to solve economic load dispatch problems. Int J Electr Power Energy Syst 57:178–188
Mohammed AA, Mohammed AA, Ahamad TK, Asaju LB (2016) Tournamentbased harmony search algorithm for nonconvexeconomic load dispatch problem. Appl Soft Comput 47:449–459
Zare K, Haque MT, Davoodi E (2012) Solving nonconvex economic dispatch problem with valve point effects using modified group search optimizer method. Electr Power Syst Res 84(1):83–89
Kasmaei MP, Nejad MR (2011) An effortless hybrid method to solve economic load dispatch problem in power systems. Energy Convers Manag 52:2854–2860
Chokpanyasuwan C, Anantasate S, Pothiya S, Pattaraprakom W, Bhasaputra P (2009) Honey bee colony optimization to solve economic dispatch problem with generator constraints. IEEE ECTIConference
Kuo CC (2008) A novel coding scheme for practical economic dispatch by modified particle swarm approach. IEEE Trans Power Syst 23:1825–1835
Aydın D, Liao T, Montes M, Stützle T (2011) Improving performance via population growth and local search the case of the artificial bee colony algorithm. In: International conference on artificial evolution, pp 85–96
Wang L, Li L (2013) An effective differential harmony search algorithm for the solving nonconvex economic load dispatch problems. Electr Power Energy Syst 44:832–843
Acknowledgements
Not applicable.
Funding
This research received no specific grant from any funding agency in the public, commercial, or notforprofit sectors.
Author information
Authors and Affiliations
Contributions
We hereby declare that both authors contributed to the design and implementation of the research, whereas PV (Pooja Verma) conducted literature review, interpretation of the data and provide the resources for the paper and RPP (Raghav Prasad Parouha) contributed to the analysis of the results and to the writing of the manuscript as well as implementation of the simulation model in the C language environment. The manuscript has been read and approved by all named authors, and there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Verma, P., Parouha, R.P. An advanced hybrid metaheuristic algorithm for solving small and largescale engineering design optimization problems. Journal of Electrical Systems and Inf Technol 8, 10 (2021). https://doi.org/10.1186/s4306702100032z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s4306702100032z
Keywords
 Global optimization
 Small and largescale optimization
 Metaheuristics
 Hybrid algorithm