摘要
针对郊狼优化算法全局搜索能力不足、易陷入局部最优的缺陷进行了研究,在郊狼优化算法寻优进程中引入变形的精英保留策略,并在郊狼成长过程中加入环境影响因子,再将成长后的郊狼代入Kent映射遍历搜索空间,强化算法的开采能力和搜索性能,提出了一种改进郊狼优化算法,数值实验表明该算法具有较优的性能。以全社会经济成本和碳排放量为决策目标,构建了一个电动汽车充电站选址定容双层规划模型,并将改进后的郊狼优化算法求解该规划模型,验证了该算法的可行性和有效性。
For coyotes optimization algorithm’s global search ability was insufficient,easily plunged into local optimum,this paper proposed an improved coyote optimization algorithm.In the optimization process of coyote optimization algorithm,this improved algorithm introduced a deformed elite retention strategy and added the environmental impact factor in the growth process of coyote,then brought the grown coyotes into the Kent mapping to traverse the search space to strengthen the mining ability and search performance of the algorithm.Numerical experiments show that the improved algorithm has better perfor-mance.Taking the whole social and economic costs and carbon emissions as the decision-making objectives,this paper constructed a bi-level programming model for location and capacity of electric vehicle charging station,and used the improved coyote optimization algorithm to solve the programming model to verify the feasibility and effectiveness of the algorithm.
作者
罗佳欣
何登旭
Luo Jiaxin;He Dengxu(School of Mathematics&Physics,Guangxi University for Nationalities,Nanning 530006,China)
出处
《计算机应用研究》
CSCD
北大核心
2022年第3期751-757,共7页
Application Research of Computers
基金
国家自然科学基金资助项目(11961006)
广西高校科学技术研究重点资助项目(2013ZD022)。
关键词
郊狼优化算法
环境影响因子
Kent映射
精英保留
双层规划
选址定容
coyote optimization algorithm
environmental impact factor
Kent mapping
elitism selection
bi-level programming
location and capacity determination
