TY - JOUR
T1 - Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems
AU - Gupta, Shubham
AU - Abderazek, Hammoudi
AU - Yıldız, Betül Sultan
AU - Yildiz, Ali Riza
AU - Mirjalili, Seyedali
AU - Sait, Sadiq M.
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/11/30
Y1 - 2021/11/30
N2 - Determining 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 optimization (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.
AB - Determining 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 optimization (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.
KW - Exploitation
KW - Exploration
KW - Mechanical design problems
KW - Metaheuristic algorithms
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85113428089&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2021.115351
DO - 10.1016/j.eswa.2021.115351
M3 - Article
AN - SCOPUS:85113428089
SN - 0957-4174
VL - 183
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 115351
ER -