基于协作策略的量子粒子群优化算法

Di Zhou, Jun Sun, Wenbo Xu
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引用次数: 7

摘要

本文首次从分布估计算法(EDAs)的角度对量子粒子群优化算法(QPSO)进行了研究,证明了QPSO是EDA和标准粒子群优化算法(SPSO)的结合。此外,提出了一种新型的协同量子粒子群优化算法(CQPSO),以防止进化算法由于多样性快速下降而普遍存在过早收敛的趋势。它是一种将多个QPSO算法在频繁重组的子群中单独模拟的并行算法,对消息的传递起着重要的作用。通过实验研究了该算法最有效的通信频率和各子群大小的设置。我们的实验还表明,CQPSO能够比原QPSO和SPSO找到更好的解,并且效率更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An advanced Quantum-behaved Particle Swarm Optimization algorithm utilizing cooperative strategy
In this paper, Quantum-behaved Particle Swarm Optimization algorithm (QPSO) is investigated from the perspective of Estimation of Distribution Algorithms (EDAs) for the first time, which proves that QPSO is a combination of EDA and Standard Particle Swarm Optimization algorithm (SPSO). Additionally, a novel cooperative quantum-behaved particle swarm optimization algorithm (CQPSO) is proposed to prevent the Evolutionary Algorithms' universal tendency of premature convergence as a result of rapid decline in diversity. It is a type of parallel algorithm in which several QPSO algorithms are simulated individually in sub-swarms with frequent recombination which plays a roll of message passing. The most effective settings of Communication Frequency and the Size of Each Sub-Swarm for this novel algorithm are studied through experiments. Our experiments also show that CQPSO is able to find better solutions than the original QPSO and SPSO with higher efficiency.
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