Neighborhood learning bacterial foraging optimization for solving multi-objective problems

Abstract

Based on the concept of neighborhood learning, this paper proposes a novel heuristic algorithm which is called Neighborhood Learning Multi-objective Bacterial Foraging Optimization (NLMBFO) for solving Multi-objective problems. This novel algorithm has two variants: NLMBFO-R and NLMBFO-S, using ring neighborhood topology and star neighborhood topology respectively. Learning from neighborhood bacteria accelerates the bacteria to approach the true Pareto front and enhances the diversity of optimal solutions. Experiments using several test problems and well-known algorithms test the capability of NLMBFOs. Numerical results illustrate that NLMBFO performs better than other compared algorithms in most cases.