引用本文:李钊,袁文浩,任崇广.基于搜索空间划分与Canopy K-means聚类的种群初始化方法[J].控制与决策,2020,35(11):2767-2772
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基于搜索空间划分与Canopy K-means聚类的种群初始化方法
李钊,袁文浩,任崇广
(山东理工大学计算机科学与技术学院,山东淄博255000)
摘要:
为了提高差分进化算法对搜索空间的探索与开发能力,提高差分进化算法的收敛性与算法的进化效率,提出一种基于搜索空间均匀划分与局部搜索和聚类相结合的种群初始化方法.该方法首先对决策变量空间进行均匀划分,并从各个子空间中随机选择一个个体,得到的个体能够覆盖整个搜索空间;然后,利用Hooke-Jeeves算法对各子空间进行局部搜索得到局部最优的个体,并结合改进的Canopy算法与K-means聚类算法,辨识搜索空间中的前景区域,以此为基础对局部搜索产生的局部最优个体进行筛选,最终生成初始种群中的个体.通过与其他种群初始化方法对CEC2017中5个测试函数进行实验对比,所提出的方法的运行时间可缩减为已有方法的0.75倍,适应度函数可减少为已有方法的0.03倍,且具有最小的标准差以及最优的收敛特性.
关键词:  种群初始化  搜索空间划分  Hooke-Jeeves算法  局部寻优  K-means聚类
DOI:10.13195/j.kzyjc.2019.0358
分类号:TP18
基金项目:国家自然科学基金项目(61701286);山东省自然科学基金项目(ZR2018LF002,ZR2017LF004);淄博市校城融合项目(2018ZBXC021).
Population initialization based on search space partition and Canopy K-means clustering
LI Zhao,YUAN Wen-hao,REN Chong-guang
(College of Computer Science and Technology,Shandong University of Technology,Zibo255000,China)
Abstract:
In order to improve the ability of exploration and exploitation and improve the convergence and evolutionary efficiency for differential evolution algorithms, a population initialization method based on uniform partition of search space, local search and clustering is proposed. Firstly, the decision variable space is partitioned uniformly, and an individual is randomly selected from each subspace, and the selected individuals can cover the whole search space. Then Hooke-Jeeves algorithm is used to search each subspace locally, and the local optimal individuals are got. Combined with the improved Canopy algorithm and K-means clustering algorithm, the promising region in the search space is identified. Based on this, the local optimal individuals generated by local search are screened, and the individuals for the initial population are finally generated. Compared with other population initialization methods for five CEC2017 test functions, the running time of the proposed method can be reduced to 0.75 times, and the fitness function can be reduced to 0.03 times that of the existing methods. And the proposed method has the minimum standard deviation and the optimal convergence characteristics.
Key words:  population initialization  search space partition  Hooke-Jeeves algorithm  local optimization  K-means clustering

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