Cultural Particle Swarm Optimization Algorithms for Interval Multi-Objective Problems

Abstract

Traditional dominant comparison never fits for the interval multi-objective optimization problems. The particle swarm optimization for solving these problems cannot adaptively adjust the key parameters and easily falls into premature. So a novel multi-objective cultural particle optimization algorithm is proposed. Its strength are: (i)The possibility degree is introduced to construct a novel dominant relationship so as to rationally measure the uncertainty of particles; (ii)The grid’s coverage degree is defined based on topological knowledge and used to measure the uniformity of non-dominant solutions instead of the crowding distance. (iii)The key flight parameters are adaptively adjusted and the local or global best are selected in terms of the knowledge. Simulation results indicate that the proposed algorithms coverage to the Pareto front uniformly and the uncertainty of non-dominant solutions is less. Furthermore, the knowledge plays a rational impact on balancing exploration and exploitation.