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为了提高克隆选择算法对复杂多模态函数优化问题的全局最优解搜索能力,基于“Stretching”拉伸技术提出了一种拉伸克隆选择算法(SCSA),该技术能在算法的搜索过程中,不断缩小目标函数局部极值点的搜索范围,从而提高算法的优化效率.为增加算法种群的多样性及提高算法的全局收敛性,算法中引入了混沌变异机制和基于抗体的浓度及亲和度差的选择机制.多模态函数优化实验结果表明,基于该技术的SCSA算法相比传统的人工免疫算法能有效地抑制早熟收敛,具有更好的收敛速度和精度,是一种有效的多模态函数优化算法.
In order to improve the global optimal solution search ability of the clonal selection algorithm for complex multi-modal function optimization problems, a stretched clone selection algorithm (SCSA) is proposed based on the Stretching , The search range of the local extreme points of the objective function is continuously narrowed so as to improve the optimization efficiency of the algorithm.In order to increase the diversity of the algorithm population and improve the global convergence of the algorithm, the chaotic mutation mechanism and the antibody-based concentration The results show that the SCSA algorithm based on this technique can effectively suppress the premature convergence compared with the traditional artificial immune algorithm, and has better convergence speed and accuracy, which is an effective Multi-modal function optimization algorithm.