期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
卷 41, 期 4, 页码 1003-1014出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2010.2103055
关键词
Local diversity; particle swarm optimizer (PSO); reconstruction technique; territory
类别
资金
- National Natural Science Foundation of China [60871096, 60835004]
- Ministry of Education of China through Ph.D. Programs Foundation [200805320006]
Particle swarm optimizer (PSO) is a powerful optimization algorithm that has been applied to a variety of problems. It can, however, suffer from premature convergence and slow convergence rate. Motivated by these two problems, a hybrid global optimization strategy combining PSOs with a modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is presented in this paper. The modified BFGS method is integrated into the context of the PSOs to improve the particles' local search ability. In addition, in conjunction with the territory technique, a reposition technique to maintain the diversity of particles is proposed to improve the global search ability of PSOs. One advantage of the hybrid strategy is that it can effectively find multiple local solutions or global solutions to the multimodal functions in a box-constrained space. Based on these local solutions, a reconstruction technique can be adopted to further estimate better solutions. The proposed method is compared with several recently developed optimization algorithms on a set of 20 standard benchmark problems. Experimental results demonstrate that the proposed approach can obtain high-quality solutions on multimodal function optimization problems.
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