Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (28): 58-60.
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KONG Qing-qin,SUN Jun,XU Wen-bo
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孔庆琴,孙 俊,须文波
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Abstract: This paper proposes a new searching strategy based on QPSO(Quantum-behaved Particle Swarm Optimization).Instead of each dimension of a particle learns from just himself historical best information,each particle learns from different particle’s historical best information in the new strategy.The new strategy ensures that the diversity of the swarm is preserved to discourage premature convergence.In addition,the new algorithm does not introduce any complex computation.After that,we test the revised QPSO algorithm on several benchmark functions and the experiment results show the priority.
Key words: Quantum-behaved PSO, searching strategy, premature, computation
摘要: 基于量子行为的粒子群优化算法(Quantum-behaved Particle Swarm Optimization,QPSO)提出一种新的搜索策略。在新的搜索策略中,粒子的每一维不再是只通过自身的信息进行下一步的搜索,而是某些维通过其他粒子的信息进行搜索。新的搜索策略确保了种群的多样性,很好地避免了早熟现象,并且没有引进多余的计算。用几个基准函数测试了改进的QPSO算法,实验结果表明了它的优越性。
关键词: 量子行为的粒子群优化算法, 搜索策略, 早熟, 计算
KONG Qing-qin,SUN Jun,XU Wen-bo. Improved algorithm based on quantum-behaved particle swarm optimization[J]. Computer Engineering and Applications, 2007, 43(28): 58-60.
孔庆琴,孙 俊,须文波. 基于QPSO的改进算法[J]. 计算机工程与应用, 2007, 43(28): 58-60.
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