基于个体优化的自适应小生境遗传算法

Adaptive Niche Genetic Algorithm Based on Individual Optimization

针对遗传算法在处理复杂多峰函数优化问题时易于早熟和局部搜索能力差等问题,提出一种基于个体优化的自适应小生境遗传算法。在自适应小生境的基础上,利用进化过程中相邻个体的信息产生的试探点标记的算法进化方向,缩短邻域搜索的区间,提高算法的局部搜索能力。对复杂多峰问题进行的优化实验结果证明,该算法能快速可靠地收敛到全局最优解,其收敛速度和解精度均优于简单遗传算法和其他小生境算法。

To solve the problems of Genetic Algorithm（GA） which is used to seek the global optimums in multimodal-function-optimization, an Adaptive Niche Genetic Algorithm based on Individual Optimization（IOANGA） is proposed. The IOANGA, which is based on adaptive Niche GA, makes use of the information of the nearest individuals generated in the process of evolution to shrink the search space and improve the ability of local search. Experimental results show that IOANGA is a much more competent optimization method than GA and other Niche methods.