Review of Multi-Objective Swarm Intelligence Optimization Algorithms

Multi-objective swarm intelligence (MOSI) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) that consist of two or more conflict objectives, in which improving an objective leads to the degradation of the other. The MOSI algorithms were based on the integration of single objective algorithms and multi-objective optimization (MOO) approaches. The MOO approaches included 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, artificialbeecolony,fireflyalgorithm.batalgorithm,gravitational search algorithm, grey wolf optimizer, bacterial foraging, and moth-flame optimization algorithms, were reviewed. These reviewed algorithms were mainly developed to solve continuous MOPs. The review was based on how the algorithms dealt with objective functions using MOO approaches, the benchmark MOPs used in the evaluation and performance metrics. Furthermore, it described the advantages and disadvantages of each MOO approach and provides some possible future research directions in this area. The results showed that several MOO approaches were used in most of the proposed MOSI algorithms. Integrating other different MOO approaches might help in developing more effective optimization algorithms, especially in solving complex MOPs. Furthermore, most of the MOSI algorithms were evaluated using MOPs with two objectives, which clarified open issues in this research area.