Gupta, ShubhamAbderazek, HammoudiMirjalili, SeyedaliSait, Sadiq M.2024-01-222024-01-222021-11-30Yıldız, B. S. vd. (2021). "Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems". Expert Systems with Applications, 183.0957-41741873-6793https://www.sciencedirect.com/science/article/pii/S095741742100779Xhttps://hdl.handle.net/11452/39221Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine metaheuristic algorithms, namely, salp swarm algorithm (SSA), multi-verse optimizer (MVO), moth-flame optimizer (MFO), atom search optimi-zation (ASO), ecogeography-based optimization (EBO), queuing search algorithm (QSA), equilibrium optimizer (EO), evolutionary strategy (ES) and hybrid self-adaptive orthogonal genetic algorithm (HSOGA). The efficiency of these algorithms is evaluated on eight mechanical design problems using the solution quality and convergence analysis, which verifies the wide applicability of these algorithms to real-world application problems.eninfo:eu-repo/semantics/closedAccessOptimizationMetaheuristic algorithmsMechanical design problemsExplorationExploitationDifferential evolutionGenetic algorithmsQuality controlAlgorithm for solvingDesign problemsMechanical designMetaheuristic optimizationOptimisationsOptimization algorithmsOptimizersConstrained optimizationArticle0006920663000062-s2.0-85113428089183https://doi.org/10.1016/j.eswa.2021.115351Computer science, artificial intelligenceEngineering, electrical & electronicOperations research & management scienceMetaheuristics; Fireflies; Chiroptera