A Multi-objective Binary Bat Algorithm
Laamari Mohamed Amine, Nadjet Kamel · 2015
Bat Algorithm (BA) is a recently proposed heuristic algorithm based on the echolocation behavior of bats. BA has proven to have better performance than other well-known algorithms like particle swarm optimization (PSO) and genetic algorithm (GA). However, the research on using BA for binary and multi-objective optimization has just begun. In this paper we propose a multi-objective binary bat algorithm (MBBA) for multi-objective optimization in binary search space. The algorithm uses a modified bat position updating strategy which works better with binary problems, a mutation operator is introduced to improve the local search ability and help the diversity of algorithm, a Pareto dominance based approach with external elitist archive to find optimal Pareto solutions, and a flight leader selection approach to help bat ight. An experimental study is done on multiple multi-objective benchmarks to test the performance of the proposed algorithm and compare it to the performance of non-dominated sorting genetic algorithm II (NSGA-II). The experimental results show that the proposed MBBA is a competitive multi-objective algorithm and outperforms NSGA-II.