Multiobjective Particle Swarm Optimization with Improved Selection Strategy for Route Optimization

The Multiobjective Route Optimization (MORO) problem is an extension of the traditional single-object route optimization problem, which aims to find an effective path with several conflicting objectives. Many research studies used multiobjective evolutionary algorithm (MOEA) to solve MORO problem, however, they cannot achieve satisfactory results in both quality and computational speed. In this paper, an improved selection strategy based multiobjective particle swarm optimization (ISSMOPSO) is proposed for MORO. The improved selection strategy tactfully combines the advantages of vector evaluated genetic algorithm (VEGA) and Pareto dominating and dominated relationship based fitness function (PDDR-FF). The selection strategy based on VEGA has a preference for the edge region of the Pareto front, the PDDR-FF-based selection strategy has the tendency converging toward the center area of the Pareto front, which preserve both the convergence rate and the distribution performance. The experimental results show that the convergence of ISSMOPSO is better than comparison algorithms, and the diversity is close to NSGA-II and SPEA2 but better than traditional MOPSO.