一种多目标量子算术优化算法探究
Study on a Multi-objective Quantum Arithmetic Optimization Algorithm
陈超 1赵海涛 1王臻1
作者信息
- 1. 国网浙江省电力有限公司信息通信分公司,浙江杭州 310000
- 折叠
摘要
为更好地解决算术算法中多 目标优化问题,提出一种基于档案集的多 目标量子算术优化算法并在七个基准函数上进行测试;通过分析测试结果以及与常用的多 目标优化方法/多 目标粒子群优化算法、快速非支配排序遗传算法和多 目标灰狼优化算法相关指标进行比较,表明了该算法优于上述常用的多 目标优化算法,并具有较高的收敛速率.
Abstract
To better solve the multi-objective optimization problems in arithmetic algorithms,a multi-objective quantum arithmetic optimization algorithm based on archive sets is proposed and tested on seven benchmark functions.By analyzing the test results and comparing them with the commonly used multi-objective optimization methods/multi-objective particle swarm optimization algorithms,fast non-dominated sorting genetic algorithms,and multi-objective grey wolf optimization algorithms,it is shown that this algorithm is superior to the aforementioned commonly used multi-objective optimization algorithms,with a higher convergence rate.
关键词
算术优化算法/多/目标优化/量子算术优化算法/元启发式方法/群智能优化Key words
arithmetic optimization algorithm/multi-objective optimization/quantum arithmetic optimization algorithm/metaheuristic method/swarm intelligence optimization引用本文复制引用
出版年
2024
NETL
NSTL
万方数据