用于函数优化的混沌克隆选择算法

Hongwei Dai, Yu Yang, Cunhua Li
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引用次数: 1

摘要

在克隆选择算法(CSA)中引入混沌初始化和混沌搜索,克服了传统克隆选择算法中存在的抗体随机初始化和过早收敛问题。不同亲和度的抗体充分利用混沌变量的遍历性和随机性,进行混沌搜索,利用局部解空间。在测试函数上的实验结果表明,混沌CSA优于经典克隆选择算法。
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Chaotic Clonal Selection Algorithm for function optimization
The chaotic initialization and chaotic search are introduced into Clonal Selection Algorithm (CSA) to overcome random antibody initialization and premature convergence problems in traditional CSA. Taking full advantages of the ergodic and stochastic properties of chaotic variables, antibodies with different affinity perform chaotic search to exploit local solution space. Experimental results on test functions demonstrate that the chaotic CSA outperforms the classical clonal selection algorithm.
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