改进自适应遗传算法求解函数优化问题  

The function optimization problem solved by the improved adaptive Genetic Algorithm

作　　者：邵记安 张宇辉 魏文红 SHAO Ji-an;ZHANG Yu-hui;WEI Wen-hong(College of Computer Science and Technology,Dongguan Institute of Technology,Dongguan 523808,Guangdong Province,China)

机构地区：[1]东莞理工学院计算机科学与技术学院,广东东莞523808

出　　处：《信息技术》2024年第7期96-103,108,共9页Information Technology

基　　金：国家自然科学基金(62106046);广东省自然科学基金资助项目(2019A1515110474)。

摘　　要：针对遗传算法在复杂函数优化过程中存在收敛速度慢、搜索效率低和易陷入局部最优等问题,提出一种多方面改进的自适应遗传算法。算法从全局出发,对编码长度、种群初始化方式、选择方式、交叉和变异算子自适应机制以及适应度函数构造方式等方面进行了优化。仿真实验表明,算法在收敛速度、求解精度、稳定性、全局寻优能力等方面有了明显的改善,在复杂函数优化问题中显示了较好的性能。To solve the problems of slow convergence speed,low search efficiency and the ease of falling into local optimum in the process of complex function optimization of genetic algorithm,an improved adaptive genetic algorithm is proposed.The algorithm optimizes the coding length,population initialization method,selection method,crossover and mutation operator adaptation mechanism and fitness function construction method from the global perspective.The simulation results show that the algorithm has a significant improvement in convergence speed,solution accuracy,stability,global optimization ability and so on,and shows excellent performance in complex function optimization problems.

关 键 词：遗传算法 种群初始化 收敛速度 自适应 函数优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]