Adaptive multi-objective particle swarm optimization using three-stage strategy with decomposition

Balancing the convergence and the diversity is one of the crucial researches in solving multi-objective problems (MOPs). However, the optimization algorithms are inefficient and require massive iterations. The convergence accuracy and the distribution of the obtained non-dominated solutions are defective in solving complex MOPs. To solve these problems, a novel adaptive multi-objective particle swarm optimization using a three-stage strategy (tssAMOPSO) is proposed in this paper. Firstly, an adaptive flight parameter adjustment is proposed to manage the states of the algorithm, switching between the global exploration and the local exploitation. Then, the three-stage strategy, including adaptive optimization, decomposition, and Gaussian attenuation mutation, is conducted by population in each iteration. The three-stage strategy remarkably promotes the diversity and efficiency of the optimization process. Furthermore, the convergence analysis of three-stage strategy is provided in detail. Finally, particles are equipped with memory interval to improve the reliability of personal best selection. In the maintenance of external archive, the proposed fusion index can enhance the quality of non-dominated solutions directly. A series of benchmark instances, ZDT and DTLZ test suits, are used to verify the performance of tssAMOPSO. Several classical and state-of-the-art algorithms are employed for experimental comparisons. Experimental results show that tssAMOPSO outperforms the other algorithms and achieves admirable comprehensive performance.