Abstract Aiming at premature convergence and the slow search speed of the traditional particle swarm optimization, a heterogeneous multiple population strategy is combined with the characteristics of dynamic probabilistic particle swarm optimization (DPPSO). In the evolutionary process of DPPSO with the strategy, multiple sub-populations are maintained and each sub-population evolves with different DPPSO variants. According to certain rules, communication between the sub-populations are executed to maintain the information exchange inside the entire population and coordinate exploration and exploitation. DPPSO algorithms with the strategy are tested on four benchmark functions which are commonly used in the evolutionary computation. Experimental results demonstrate that the DPPSO with the strategy significantly improves the convergence speed and stability with strong global search ability.
NI Qing-Jian,DENG Jian-Ming,XING Han-Cheng. Dynamic Probabilistic Particle Swarm Optimization Based on Heterogeneous Multiple Population Strategy[J]. , 2014, 27(2): 146-152.
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2014, Vol. 27 
