Journal
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Volume 29, Issue 6, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218001415590090
Keywords
Constrained optimization; differential evolution; good point set; multi-objective optimization; particle swarm optimization
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Funding
- National Natural Science Foundation of China [60934002, 61071029]
- Key Laboratory Project of Guangxi [YQ15103, YQ15116]
- Scientific Research Fund of Hunan Provincial Education Department [13C333]
- Science and Technology Research Foundation of Hunan Province [2014GK3043]
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In attempting to overcome the limitation of current methods to solve complicated constrained optimization problems, this paper proposes an adaptive hybrid particle swarm optimization multi-objective optimization (AHPSOMO) algorithm. In the early stage, this algorithm initializes the individuals in a population in an even manner using good point set (GPS) theory so that the diversity of the population can be guaranteed. In the process of local search, differential evolution (DE) algorithm is introduced for updating local optimal individuals. Particle swarm optimization method is further adopted to conduct global search as per the multi-objective approach. The results of simulation tests on 24 classic test functions and three engineering constrained optimization problems show that compared with other algorithms, our proposed algorithm is effective and feasible, which can offer highly accurate solutions with good robustness.
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