Multi-Objective Material Generation Algorithm (MOMGA) for Optimization Purposes

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Nouhi, Behnaz, Khodadadi, Nima, Azizi, Mahdi, Talatahari, Siamak, Gandomi, Amir H.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Chemical compounds; Chemical reactions; Competitions on Evolutionary Computation; Decision making; Evolutionary computation; Heuristic algorithms; Heuristic methods; Iterative methods; Machine learning algorithms; Material Generation Algorithm; Metaheuristics; Multi-objective optimization; Multiple objective analysis; Optimization; Performance evaluation; Real-world engineering problems
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3211529

Optimization is a process of decision-making in which some iterative procedures are conducted to maximize or minimize a predefined objective function representing the overall behavior of a considered system problem. Most of the time, one specific function cannot represent the overall behavior of a system with particular levels of complexity, so the multiple objective functions should be determined for this purpose which requires an algorithm with adaptability to this situation. Multi-objective optimization is a process of decision making in which maximization or minimization of multiple objective functions is considered for reaching the acceptable levels of performance for the considered system problem. In this paper, the multi-objective version of the Material Generation Algorithm (MGA) is proposed as MOMGA, one of the recently developed metaheuristic algorithms for single-objective optimization. To evaluate the overall performance of the MOMGA, the benchmark multi-objective optimization problems of the Competitions on Evolutionary Computation (CEC) are considered alongside the real-world engineering problems. Based on the results, the MOMGA is capable of providing very acceptable results in dealing with multi-objective optimization problems.