A Comparative Study of Recent Non-traditional Methods for Mechanical Design Optimization

Ali Riza Yildiz, Hammoudi Abderazek, Seyedali Mirjalili

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Solving practical mechanical problems is considered as a real challenge for evaluating the efficiency of newly developed algorithms. The present article introduces a comparative study on the application of ten recent meta-heuristic approaches to optimize the design of six mechanical engineering optimization problems. The algorithms are: the artificial bee colony (ABC), particle swarm optimization (PSO) algorithm, moth-flame optimization (MFO), ant lion optimizer (ALO), water cycle algorithm (WCA), evaporation rate WCA (ER-WCA), grey wolf optimizer (GWO), mine blast algorithm (MBA), whale optimization algorithm (WOA) and salp swarm algorithm (SSA). The performances of the algorithms are tested quantitatively and qualitatively using convergence speed, solution quality, and the robustness. The experimental results on the six mechanical problems demonstrate the efficiency and the ability of the algorithms used in this article.

Original languageEnglish
JournalArchives of Computational Methods in Engineering
DOIs
Publication statusAccepted/In press - 1 Jan 2019
Externally publishedYes

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