Guided Manta Ray foraging optimization using epsilon dominance for multi-objective optimization in engineering design. (1st March 2022)

• Record Type: Journal Article
• Authors: Zouache, Djaafar; Abdelaziz, Fouad Ben
• Is Part Of: Expert systems with applications. Volume 189(2022)
• Publication Date: 2022-03-01
• Subjects: Multi-objective optimization -- Pareto set approximation -- Epsilon-dominance -- Manta Ray foraging optimization -- Engineering design; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.116126
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: In recent decades, metaheuristics have proven their effectiveness in solving large-scale real-world problems with multiple objectives. However, we still need to design robust algorithms capable of converging and approximating efficiently the true Pareto set. In this paper, we extend the recently Manta Ray foraging optimization (MOMRFO) to the multiobjective case. MOMRFO uses a population archive to store the non-dominated solutions generated so far by the exploration process. The leader's solutions are selected from the population archive to guide the Manta Rays population towards promising search regions. We use crowding distance and ε -dominance to provide a good compromise between diversity and convergence of the obtained potential Pareto set. The proposed algorithm is validated on five bi-objective test functions, seven three objective test functions, and is applied to structural design problems such as four-bar truss design, speed reduced design, welded beam design, and disk brake design. The algorithm is compared with four well-known multi-objective meta-heuristics. The experimental results show that the MOMRFO algorithm outperforms against the selected multiobjective meta-heuristics by providing better convergence behaviour with a better diversity of solutions. Highlights: We propose a new multi-objective Manta-Ray Algorithm for design engineering. We use the population archive and the leader's solutions to guide the Heuristic. We use the crowding distance and the epsilon dominance to approximate Pareto set. Our algorithm provides a better convergence behaviour with better diverse solutions.