一种基于种群熵的混沌小世界优化算法

Chaos Small-World Optimal Algorithm Based on Population Entropy

针对小世界算法在复杂函数优化中存在的停滞现象,以及低局域短连接的搜索效率问题,提出了一种基于种群熵的混沌小世界算法.根据信息熵建立了种群个体浓度,并以个体浓度和个体适应度作为评价标准进行高浓度的个体更替,从而实现了种群的自我调节和多样性保持.利用混沌变量的遍历性和随机性,通过Logistic映射生成初始种群,采用混沌扰动对短连接后的个体进行局部搜索,从而提高了小世界算法的搜索效率和搜索精度.试验结果表明,该算法不仅明显改善了小世界算法的搜索能力,而且搜索效率也得到了显著提高.

To avoid trapping into local minimum and improve searching efficiency of local shortrange operator during the function optimization, a chaos small-world optimal algorithm based on population entropy is presented. The individual density, constructed according to the information entropy, and fitness are taken as the evaluation criterion, and the individuals of high density are replaced by new initial individuals, which achieve the self-adjustability and diversity of population. The characteristics of ergodicity and randomness of chaotic variables are considered to produce the initial population with logistic mapping, and the individual local search is performed by chaos disturbance after local short-range search, thus the searching efficiency and accuracy are obviously heightened. The simulation results show that the proposed algorithm remarkably improved the searching capacity and efficiency in small-world algorithm.