A multi-objective jumping particle swarm optimization algorithm for the multicast routing

Abstract

This paper presents a new multi-objective jumping particle swarm optimization (MOJPSO) algorithm to solve the multi-objective multicast routing problem, which is a well-known NP-hard problem in communication networks. Each particle in the proposed MOJPSO algorithm performs four jumps, i.e. the inertial, cognitive, social and global jumps, in such a way, particles in the swarm follow a guiding particle to move to better positions in the search space. In order to rank the non-dominated solutions obtained to select the best guider of the particle, three different ranking methods, i.e. the random ranking, an entropy-based density ranking, and a fuzzy cardinal priority ranking are investigated in the paper. Experimental results show that MOJPSO is more flexible and effective for exploring the search space to find more nondominated solutions in the Pareto Front. It has better performance compared with the conventional multi-objective evolutionary algorithm in the literature.