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Table 9 Optimum results for pressure vessel design problem

From: An advanced hybrid meta-heuristic algorithm for solving small- and large-scale engineering design optimization problems

Algorithms Optimal values for variables Optimal cost
Ts (\({x}_{1}\)) Th(\({x}_{2}\)) R(\({x}_{3}\)) L(\({x}_{4}\))
PSO 0.8125 0.4375 42.091266 176.7465 6061.0777
DE 0.8125 0.4375 42.098411 176.637690 6059.7340
ACO 0.8125 0.4375 42.103624 176.572656 6059.0888
ABC 0.812500 0.437500 42.098446 176.636596 6059.714339
GWO 0.8125 0.4345 42.089181 176.75731 6051.5639
DA 0.782825 0.384649 40.3196 200 5923.11
GA 0.752362 0.399540 40.452514 198.00268 5890.3279
HS 1.099523 0.906579 44.456397 179.65887 6550.0230
CS 0.812500 0.437500 42.0984456 176.6363595 6059.7143348
EO 0.7781 0.3846 40.319619 199.99999 5885.3279
CDE 0.8125 0.4375 42.098411 176.63769 6059.7340
CSDE 0.8125 0.4375 42.10 176.6 6060.0000
CPSO 0.8125 0.4375 42.091266 176.746500 6061.0777
IPSO 0.812500 0.437500 42.098445 176.6365950 6059.7143
CSKH 0.7781686 0.3846491 40.3196187 200.0000 6059.7010
aDE 0.8125 0.4375 40.32962 179.85887 5885.3279
aPSO 0.8125 0.4375 40.31962 179.85887 5885.3079
haDEPSO 0.8125 0.4375 40.31962 179.65887 5882.4387