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Title: Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems
Authors: Gupta, Shubham
Abderazek, Hammoudi
Yıldız, Betül Sultan
Yildiz, Ali Riza
Mirjalili, Seyedali
Sait, Sadiq M.
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Gupta, S., Abderazek, H., Yıldız, B. S., Yildiz, A. R., Mirjalili, S. & Sait, S. M. (2021). Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems. Expert Systems With Applications, 183, 115351-.
Journal: Expert Systems with Applications 
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 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.
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2021.115351
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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