Comparison of Current Metaheuristics for Solving Engineering Problems

Nowadays, with the increasing number of metaheuristic, different strategies for solving engineering problems have been introduced. In this study, the Crayfish Optimizer Algorithm (COA), Spider Wasp Optimizer (SWO) and Goose Algorithm (Goose) are compared among the current metaheuristics inspired by the unique behaviors and survival functions of different animals in nature. To the best of our knowledge, this is the first study to compare the performance of these algorithms on three popular engineering problems (pressure vessel design, welded beam design and tension-compression design problems). When the experimental results and convergence rates are evaluated, it is observed that all metaheuristics exhibit very high performance. SWO, inspired by the hunting and nesting behavior strategies of spider wasps, is the most successful metaheuristic. SWO is followed by COA, modeled from the natural behavior of crayfish, and Goose, developed by imitating the flocking behavior of goose. The statistical significance of the results was assessed using t-tests, which demonstrate the superiority of SWO. In future studies, it is aimed that other metaheuristics, especially SWO, can be used in solving different engineering problems.