Paper Info
Title
Multi-Objective Material Generation Algorithm (MOMGA) for Optimization Purposes
Abstract
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.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3211529
IEEE ACCESS
Keywords
DocType
Volume
Metaheuristics, Evolutionary computation, Chemical reactions, Chemical compounds, Heuristic algorithms, Algorithm design and analysis, Machine learning algorithms, Material generation algorithm, multi-objective optimization, real-world engineering problems, competitions on evolutionary computation
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Behnaz Nouhi100.34
Nima Khodadadi200.34
Mahdi Azizi300.68
Siamak Talatahari400.68
Amir Hossein Gandomi51836110.25