择优学习多个体差分算法求解约束优化问题

doi:10.3969/j.issn.1006-2475.2015.10.004

计算机与现代化

择优学习多个体差分算法求解约束优化问题

(1.茂名职业技术学院计算机工程系，广东茂名 525000; 2.广东省石化装备故障诊断重点实验室，广东茂名 525000)

收稿日期:2015-05-27 出版日期:2015-10-10 发布日期:2015-10-10
作者简介:付玉珍(1984-)，女，山西吕梁人，茂名职业技术学院计算机工程系讲师，硕士，研究方向：智能算法; 周洁文(1966-)，女，广东茂名人，高级讲师，本科，研究方向：图像处理; 邵龙秋，男，浙江温州人，广东省石化装备故障诊断重点实验室讲师，硕士，研究方向：智能算法，故障诊断; 张慧，女，山东德州人，讲师，硕士，研究方向：智能算法。
基金资助:
广东省石化装备故障诊断重点实验室开放基金资助项目(GDUPTKLAB201331)

Preferred Learning-based Multiple Individuals Differential Evolution Algorithm for Constrained Optimization Problems

(1. Department of Computer Engineering, Maoming Polytechnic, Maoming 525000, China; 2. Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China)

Received:2015-05-27 Online:2015-10-10 Published:2015-10-10

摘要/Abstract

摘要： 提出一种择优学习的多个体差分算法用于求解约束优化问题，目的是用来提高差分算法的搜索能力。首先，将择优学习策略应用到混合变异算子中，使其快速搜索到可行解区域，然后使用克隆策略加大对最优解区域的搜索力度，增强局部搜索能力。通过对CEC2006经典Benchmark函数测试，实验结果表明本算法在求解效率和精度上都取得了较好的结果。

关键词: 差分算法, 约束优化, 克隆, 择优学习

Abstract: This paper presents a preferred learning-based multiple individuals differential evolution algorithm for solving constrained optimization problems, in order to improve the search ability of difference evolution algorithm. Firstly, a kind of preferred learning strategy is applied to the hybrid mutation operator, which makes it quickly search to the area of feasible solution, then the clone strategies are used to enhance the search intensity in optimal solution area and the ability of local search is greatly improved. Finally the algorithm is tested on the CEC2006 classic Benchmark function, the experimental results show that the algorithm is of better results in solving efficiency and accuracy.

Key words: differential evolution algorithm, constrained optimization , clone, preferred learning

中图分类号:

TP391

付玉珍1，周洁文1，邵龙秋2，张慧1. 择优学习多个体差分算法求解约束优化问题[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2015.10.004.

FU Yu-zhen1, ZHOU Jie-wen1, SHAO long-qiu2, ZHANG Hui1. Preferred Learning-based Multiple Individuals Differential Evolution Algorithm for Constrained Optimization Problems[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.10.004.

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择优学习多个体差分算法求解约束优化问题

Preferred Learning-based Multiple Individuals Differential Evolution Algorithm for Constrained Optimization Problems

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