A Multi-objective Evolutionary Algorithm of Marriage in Honey Bees Optimization Based on the Local Particle Swarm Optimization

Marriage in Honey Bees Optimization (MBO) is a new swarm-intelligence method, but existing researches concentrate more on its application in single-objective optimization. In this paper, we focus on improving the algorithm to solve the multi-objective problem and increasing its convergence speed. The proposed algorithm is named as multi-objective Particle Swarm Marriage in Honey Bees Optimization (MOPSMBO). It uses non-dominated sorting strategy and crowded-comparison approach, utilizes the local Particle Swarm Optimization (PSO) to perform the local characteristic, and simpler the structure of MBO. Based on the Markov chain theory, we prove that MOPSMBO can converge with probability one to the entire set of minimal elements. Simulations are done on several multi-objective test functions and multi-objective Traveling Salesman Problem (TSP). By comparing MOPSMBO with MOGA, NPGA, NSGA and NSGA-II, simulation results show that MOPSMBO has better convergence speed and can better converge near the true Pareto-optimal front.