Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems

Abstract

In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeography-based optimization (BBO) to propose a hybrid algorithm termed biogeography-based particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.

Keywords:

• evolutionary algorithm
• elites
• biogeography-based optimization
• particle swarm optimization
• fuzzy strategy

Acknowledgements

Weian Guo would like to express his thanks to Shuzhi Sam Ge, of the Interactive Digital Media Institute, National University of Singapore, for his guidance and help, and also appreciates the China Scholarship Council (CSC) for supporting his expenses in Singapore.

Funding

This work is supported by the National Natural Science Foundation of China [grant numbers 70871091, 61075064, 61034004 and 61005090]; Programme for New Century Excellent Talents in University of Ministry of Education of China; PhD Programmes Foundation of Ministry of Education of China [20100072110038]; scientific research project of science and technology bureau of Jiaxing [2011BY7003]; Singapore Academic Research Fund [grants R397000139133 and R397000157112]; and NUS Teaching Enhancement [grant C397000039001].