Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems

Shubham Gupta, Hammoudi Abderazek, Betül Sultan Yıldız, Ali Riza Yildiz, Seyedali Mirjalili, Sadiq M. Sait

Research output: Contribution to journalArticlepeer-review

92 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number115351
JournalExpert Systems with Applications
Volume183
DOIs
Publication statusPublished - 30 Nov 2021

Keywords

  • Exploitation
  • Exploration
  • Mechanical design problems
  • Metaheuristic algorithms
  • Optimization

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