摘要
针对基于实数编码的遗传算法收敛速度慢与收敛精度不高等问题,通过定义种群活力,提出了一种改进的自适应遗传算法.该算法中,种群活力的定义综合考虑了种群多样性和相邻代种群间相似度,众数代替平均数作为新的种群适应度参考量,并依以上两点对交叉和变异概率进行自适应调节,同时引入并行机理对变异操作进行了改进.通过仿真实例,验证了该算法具有较高的收敛速度和求解精度.最后,该算法还被应用于解决汽油调和优化调度问题.
To solve the problems of slow convergent speed and low convergent precision in the genetic algorithm based on real coding,we define a new index to describe population evolution,population vigor,and present a revised adaptive genetic algorithm. Using the population vigor index,we consider the diversity of a population and the similarity of adjacent populations in a unified frame,and adaptively adjust the probabilities of crossover and mutation. In addition,we reset the reference value of population fitness using mode fitness instead of average fitness and improve the mutation operator using a parallel mechanism. Stimulation results show that the algorithm has a quicker convergent speed and better convergent precision. As an application example,we also employed the algorithm to solve gasoline blending recipe optimization.
出处
《信息与控制》
CSCD
北大核心
2016年第2期142-150,共9页
Information and Control
基金
国家863计划资助项目(2014AA041802-2)
关键词
遗传算法
种群活力
自适应
并行变异
油品调和
genetic algorithm
population vigor
adaptation
parallel mutation
gasoline blending