Review of multi-objective swarm intelligence optimization algorithms

Review of multi-objective swarm intelligence optimization algorithms. Journal of Information and Communication Technology, 20 (2). pp. 171-211. ISSN 2180-3862 (2021)


Multi-objective swarm intelligence (MOSI) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) that consists of two or more conflict objectives, in which improving an objective leads to the degradation of the other. The MOSI algorithms are based on the integration of single objective algorithms and multi-objective optimization (MOO) approach. The MOO approaches include scalarization, Pareto dominance, decomposition and indicator-based. In this paper, the status of MOO research and state-of-the-art MOSI algorithms namely, multi-objective particle swarm, artificial bee colony, firefly algorithm, bat algorithm, gravitational search algorithm, grey wolf optimizer, bacterial foraging and moth-flame optimization algorithms have been reviewed. These reviewed algorithms were mainly developed to solve continuous MOPs. The review is based on how the algorithms deal with objective functions using MOO approaches, the benchmark MOPs used in the evaluation and performance metrics. Furthermore, it describes the advantages and disadvantages of each MOO approach and provides some possible future research directions in this area.

Item Type: Article
Keywords: Optimization, Algorithms, Metaheuristic, Population-based, Pareto front
Taxonomy: By Subject > Computer & Mathematical Sciences > Computer Science
By Subject > Computer & Mathematical Sciences > Mathematics
Local Content Hub: Subjects > Computer and Mathematical Sciences
Depositing User: Muslim Ismail @ Ahmad
Date Deposited: 18 Feb 2022 23:59
Last Modified: 22 Feb 2022 01:47
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