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免疫算法与遗传算法都存在的不成熟收敛问题。混沌优化方法是近年出现一种新的优化技术,通常使用Logistic或Tent映射产生混沌序列进行搜索,Logistic映射产生的混沌序列的概率密度函数切比雪夫型分布,当最优值落在[0,1]的中间位置时,这种分布特性会影响全局搜索能力和效率。而Tent映射也存在迭代易落入小周期循环的问题。针对免疫算法和混沌优化算法中存在的缺陷,该文用变尺度的搜索策略,提出了一种基于Hénon映射的自适应克隆选择的优化算法,数值仿真结果表明,该文提出的算法提高了局部搜索的能力及其计算效率,算法可行有效。
Immune algorithm and genetic algorithm both exist immature convergence problem. Chaos optimization method is a new optimization technique in recent years. Logistic or Tent mapping is usually used to generate chaotic sequences. The probability density function of chaotic sequences generated by Logistic maps is Chebyshev distribution. When the optimal value falls within [0, 1] in the middle position, this distribution will affect the overall search capabilities and efficiency. Tent mapping also has the problem that iteration is easy to fall into the small cycle. In order to overcome the shortcomings of immune algorithm and chaos optimization algorithm, this paper proposes an adaptive clonal selection optimization algorithm based on Hénon mapping with variable scale search strategy. The numerical simulation results show that the proposed algorithm improves the local Search ability and computational efficiency, the algorithm is feasible and effective.