A Coupling Approach with GSO-BFOA for Many-Objective Optimization

Abstract

Glowworm swarm optimization (GSO) and bacterial foraging optimization algorithm (BFOA) are two popular swarm intelligence optimization algorithms (SIOAs). However, both GSO and BFOA show some difficulties when solving many-objective optimization problems (MaOPs). To challenge MaOPs, a coupling approach based on GSO and BFOA is proposed in this paper. To implement the coupling method, an external archive is established to save the best solutions found so far. The internal populations in GSO and BFOA can exchange the search information with the external archive in the evolutionary process. Simulation experiments are verified on two benchmark sets (DTLZ and WFG) with 3 to 15 objectives. The performance of our approach is compared with five other famous algorithms including NSGA-III, KnEA, MOEA/D-DE, GrEA and HypE. Results prove the effectiveness of our approach.