Multi-objective optimization using BFO algorithm

Authors:
Ben Niu;Hong Wang;Lijing Tan;Jun Xu
Affiliations:
Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China;College of Management, Shenzhen University, Shenzhen, China;Management School, Jinan University, Guangzhou, China;e-Business Technology Institute, The University of Hongkong, Hongkong, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

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Abstract

This paper describes a novel bacterial foraging optimization (BFO) approach to multi-objective optimization, called Multi-objective Bacterial Foraging Optimization (MBFO). The search for Pareto optimal set of multi-objective optimization problems is implemented. Compared with the proposed algorithm MOPSO and NSGAII, simulation results (measured by Diversity and Generational Distance metric) on test problems show that the proposed MBFO is able to find a much better spread of solutions and faster convergence to the true Pareto-optimal front. It suggests that the proposed MBFO is very promising in dealing with multi-objective optimization problems.