基于混沌搜索技术的免疫量子进化算法全局优化

2008 First International Conference on Intelligent Networks and Intelligent Systems Pub Date : 2008-11-01 DOI:10.1109/ICINIS.2008.135

Xiaoming You, Sheng Liu, Xiankun Sun

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引用次数: 4

摘要

提出了一种基于混沌搜索全局优化的免疫量子进化算法。首先，采用小生境方法将种群自动划分为子种群;其次，利用免疫算子和突变算子，使每个子种群得到最优解。由于量子进化算法具有内在并行性，它比经典进化算法能很好地保持种群多样性;由于采用了免疫算子和对染色体的真实表示，加快了收敛速度。介绍了提高CRIQEA性能的混沌搜索技术;基于混沌动态系统的突变算子具有摆脱局部最优的能力。仿真结果验证了CRIQEA算法的优越性。

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Immune Quantum Evolutionary Algorithm Based on Chaotic Searching Technique for Global Optimization

A novel immune quantum evolutionary algorithm based on chaotic searching for global optimization (CRIQEA) is proposed. Firstly, by niching methods population is divided into subpopulations automatically. Secondly, by using immune and catastrophe operator each subpopulation can obtain optimal solutions. Because of the quantum evolutionary algorithm with intrinsic parallelism it can maintain quite nicely the population diversity than the classical evolutionary algorithm; because of the immune operator and real representation for the chromosome it can accelerate the convergence speed. The chaotic searching technique for improving the performance of CRIQEA has been described; catastrophe operator based on chaotic dynamic systems is capable of escaping from local optima. Simulation results demonstrate the superiority of CRIQEA in this paper.

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2008 First International Conference on Intelligent Networks and Intelligent Systems

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