Adaptive multi-objective particle swarm optimization based on virtual Pareto front.

In a multi-objective particle swarm optimization (MOPSO), the selection strategies of the personal best solution (pBest) for a single particle and the global best solution (gBest) for the whole swarm are two key challenges to balance the convergence and diversity of an algorithm during its iterative process. Many selection strategies in the existing literatures were emphasized on the individual characteristics of the separate particles rather than the collective features of the whole swarm. In this paper, a novel gBest selection strategy based on a new defined virtual generational distance indicator, which is calculated from a virtual Pareto front fabricated according to the geometry of a given elite archive, is proposed for selecting the most appropriate Pareto optimal solution as the gBest with respect to the comprehensive convergence and diversity contribution to improve the search effectiveness and efficiency of a MOPSO. Besides, an adaptive pBest selection strategy based on the evolutionary state in different iterations is designed for identifying the more suitable pBest from its personal archive for each particle to strengthen the exploitation or exploration ability adaptively. The experimental results show that a MOPSO with the new gBest and pBest selection strategies outperforms ten state-of-the-art competitive algorithms on DTLZ, F, WFG and ZDT series of benchmark problems. In addition, a case study on the site selection for mobile earthquake monitoring stations is also illustrated the effectiveness of the proposed algorithm in the real-world application. [ABSTRACT FROM AUTHOR]