A quantum particle swarm optimizer with chaotic mutation operator
A quantum particle swarm optimizer with chaotic mutation operator
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DOI：
10.1016/j.chaos.2006.10.028
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发表时间：
2008-09-01
影响因子：
7.8
通讯作者：
Coelho, Leandro dos Santos
中科院分区：
文献类型：
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作者：
Coelho, Leandro dos Santos
粒子群优化（PSO）是一种基于人群的群智能算法，与进化计算技术具有许多相似之处。但是，PSO是由鸟类和其他社会生物的集体行为而不是最适合人物的生存所激发的社会心理隐喻驱动的。受经典PSO方法和量子力学理论的启发，这项工作提出了使用混乱的突变算子的新型量子行为PSO（QPSO）。基于混乱的Zaslavskii图而不是随机序列在QPSO中应用混乱序列是一种强大的策略，可以使QPSO种群多样化并改善QPSO在防止过早收敛到局部微型杂志方面的表现。仿真结果表明，QPSO在解决机械工程设计的连续优化问题方面的表现良好。 （c）2006 Elsevier Ltd.保留所有权利。
Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents a novel Quantum-behaved PSO (QPSO) using chaotic mutation operator. The application of chaotic sequences based on chaotic Zaslavskii map instead of random sequences in QPSO is a powerful strategy to diversify the QPSO population and improve the QPSO's performance in preventing premature convergence to local minima. The simulation results demonstrate good performance of the QPSO in solving a well-studied continuous optimization problem of mechanical engineering design. (C) 2006 Elsevier Ltd. All rights reserved.