摘要
为了克服遗传算法收敛慢及容易陷入局部极值等问题,文中提出了一种基于最优加权进化的自适应遗传算法。该算法将具有相同搜索结果的进化代数(记为G_f)加权处理后得到的权重与个体适应度相结合来调整变异概率,并且算法根据G_f的值自适应的调节交叉和变异的操作次序,以便提高算法的搜索性能。此外,同代所有个体在交叉操作上应具有同等地位和相同概率,从而使算法的搜索在各个方向上保持均匀性,进而增大了优秀个体的产生机率。利用四个复杂函数进行仿真测试,结果表明,该算法在收敛能力和跳出局部极值的能力方面有较大的提升。
An improved adaptive genetic algorithm is proposed to solve some problems of standard genetic algorithms,such as slow convergence and easy to fall into local extremum. In order to improve the search performance,the number of the generations by which the same optimal solution does not change（ called G_f） is combined with individual fitness to adjust the mutation probability,and the algorithm adjusts the operation order of crossover and mutation according to the G_f. In addition,the algorithm creats all individuals of each generation equally during the cross operation. The experimental results of four functions show that the proposed algorithm has a good improvement in aspects of convergent ability and the ability to jump out of local extremum.
作者
赵玉强
钱谦
ZHAO Yu-qiang;QIAN Qian(School of Information Engineering and Automation,Yunnan Key Laboratory of Computer Technology Applications,Kunming University of Science and Technology,Kunming 650500,China)
出处
《信息技术》
2018年第11期68-72,共5页
Information Technology
基金
国家自然科学基金(61462053,31300938)
云南省自然科学基金(2016FB107)
关键词
遗传算法
自适应
最优加权进化
函数优化
genetic algorithms
adaptation
optimal weighted evolution
thnetion optimization
