求解约束优化问题M-精英协同进化算法

doi:10.3969/j.issn.1001-2400.2010.05.014

J4 ›› 2010, Vol. 37 ›› Issue (5): 852-861.doi: 10.3969/j.issn.1001-2400.2010.05.014

求解约束优化问题M-精英协同进化算法

慕彩红¹;焦李成¹;刘逸^2,3

(1. 西安电子科技大学智能感知与图像理解教育部重点实验室，陕西西安 710071；
2. 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071；
3. 西安电子科技大学电子工程学院，陕西西安 710071)

收稿日期:2009-10-08 出版日期:2010-10-20 发布日期:2010-10-11
通讯作者: 慕彩红
作者简介:慕彩红(1978-)，女，讲师，博士，E-mail: caihongm@mail.xidian.edu.cn．
基金资助:
国家自然科学基金资助项目(61003199,60703108);国家863资助项目(2007AA12Z223);博士点基金资助项目(20070701022)

M-Elite coevolutionary algorithm for constrained optimization

MU Cai-hong¹;JIAO Li-cheng¹;LIU Yi^2,3

(1. Ministry of Education Key Lab. of Intelligent Perception and Image Understanding, Xidian Univ., Xi'an 710071， China;
2. State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an 710071， China;
3. School of Electronic Engineering, Xidian Univ., Xi'an 710071， China)

Received:2009-10-08 Online:2010-10-20 Published:2010-10-11
Contact: MU Cai-hong

摘要/Abstract

摘要：

提出了一种适用于约束优化问题的协同进化算法．该算法旨在模拟人类社会中团队的组建及其协作方式，并强调精英人才对团队建设的推动作用．算法将整个种群分为精英种群和普通种群，围绕各个精英来组建团队，使精英种群带动普通种群，进而带动整个种群不断进化．组建团队过程中，不同精英之间采用协作操作，精英对普通种群成员进行引导操作，其中协作操作和引导操作由若干交叉或变异算子的组合所定义．使用静态罚函数法将约束优化转化为无约束优化，利用13个约束优化测试函数对算法进行了测试．仿真实验和参数分析结果表明，该算法寻优精度高，算法稳定，运行时间少，其性能优于组织进化算法，能够有效解决复杂的约束优化问题．

关键词: 优化算法, 约束优化, 进化算法, 协同进化算法

Abstract:

An algorithm named the M-Elite Coevolutionary Algorithm (MECA) is proposed for constrained optimization problems. The algorithm simulates the establishment and cooperation of teams in the human society with emphasis on the important role of elites in teams. The whole population is divided into two subpopulations, i.e., elite population and common population, with the former leading the latter to evolve so as to accelerate the evolution of the whole population. During this process several teams are established, each elite individual acting as the core of each team. The cooperating operation is implemented between the different elite individuals, and the common individuals are led by the elite individuals in the leading operation during the process of team establishment. The above cooperating operation and the leading operation are defined by the different combinations of several crossover operators or mutation operators. Static penalty functions are used to transform a constrained optimization problem into an unconstrained one, and tests are made on 13 benchmark problems. Experimental results and parameter analysis show that the MECA can obtain a high solution quality with less runtime and is robust and that it obtains better performances than Organizational Evolutionary Algorithm, and can solve difficult constrained optimization problem.

Key words: optimization algorithms, constrained optimization, evolutionary algorithms, coevolutionary algorithms

慕彩红;焦李成;刘逸. 求解约束优化问题M-精英协同进化算法[J]. J4, 2010, 37(5): 852-861.

MU Cai-hong;JIAO Li-cheng;LIU Yi. M-Elite coevolutionary algorithm for constrained optimization[J]. J4, 2010, 37(5): 852-861.

参考文献

[1] Liu Jing, Zhong Weicai, Jiao Licheng. An Organizational Evolutionary Algorithm for Numerical Optimization [J] . IEEE Trans on Systems, Man, and Cybernetics—Part B: Cybernetics, 2007, 37(4): 1052-1064.
[2] Runarsson T P, Yao Xin. Stochastic Ranking for Constrained Evolutionary Optimization [J] . IEEE Trans on Evolutionary Computation, 2000, 4(3): 284-294.
[3] Montes E M, Coello C A C. A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems [J] . IEEE Trans on Evolutionary Computation, 2005, 9(1): 1-17.
[4] Tessema B, Yen G G. A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization [C] //IEEE Congress on Evolutionary Computation. Vancouver: IEEE, 2006: 246-253.
[5] Carlos C. Constraint-Handling Techniques Used with Evolutionary Algorithms [C／OL] . [2009-7-15] . http://doi.acm.org／10.1145／1274000.1274105
[6] Goh C K, Tan K C. A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization [J] . IEEE Trans on Evolutionary Computation, 2009, 13(1): 103-127.
[7] Hu Zhihua, Ding Yongsheng, Shao Qing. Immune Co-evolutionary Algorithm Based Partition Balancing Optimization for Tobacco Distribution System [J] . Expert Systems with Applications, 2009, 36(3): 5248-5255.
[8] 潘晓英, 焦李成. 社会协作的多智能体进化[J] . 西安电子科技大学学报, 2009, 36(2): 274-280.
Pan Xiaoying, Jiao Licheng. Social Cooperation Based Multi-agent Evolutionary Algorithm [J] .Journal of Xidian University, 2009, 36(2): 274-280.
[9] Chang W A, Ramakrishna R S. Elitism-Based Compact Genetic Algorithms [J] . IEEE Trans on Evolutionary Computation, 2003, 7(4): 367-385.
[10] 慕彩红, 焦李成, 刘逸. M-精英协同进化数值优化算法[J] . 软件学报，2009, 20(11): 2925-2938.
Mu Caihong, Jiao Licheng, Liu Yi. M-elite Coevolutionary Algorithm for Numerical Optimization[J] . Journal of Software, 2009, 20(11): 2925-2938.
[11] 慕彩红, 焦李成, 刘逸. M-精英进化算法及其在V-BLAST系统中的应用[J] . 电子与信息学报, 2009, 31(10): 2443-2448.
Mu Caihong, Jiao Licheng, Liu Yi. M-elitist Evolutionary Algorithm and Its Application in V-BLAST System [J] . Journal of Electronics ＆ Information Technology, 2009, 31(10): 2443-2448.
[12] Leung Y W, Wang Yuping. An Orthogonal Genetic Algorithm with Quantization for Global Numerical Optimization [J] . IEEE Trans on Evolutionary Computation, 2001, 5(1): 41-53.
[13] 丛琳, 沙宇恒, 焦李成. 采用正交免疫克隆粒子群算法求解SAT问题[J] . 西安电子科技大学学报, 2007, 34 (4): 616-621.
Cong Lin, Sha Yuheng, Jiao Licheng. Orthogonal Immune Clone Particle Swarm Optimization for the SAT Problem [J] . Journal of Xidian University, 2007, 34 (4): 616-621.

[1]	杨仲平,周青松,张剑云. 区域能量聚焦技术中超稀疏阵列优化算法[J]. 西安电子科技大学学报, 2021, 48(4): 57-63.
[2]	马悦,张玉梅. 面向多接入边缘计算的VNFM分布式部署方案[J]. 西安电子科技大学学报, 2021, 48(4): 20-26.
[3]	李腾,曹世杰,尹思薇,魏大卫,马鑫迪,马建峰. 应用Q学习决策的最优攻击路径生成方法[J]. 西安电子科技大学学报, 2021, 48(1): 160-167.
[4]	周杰,徐梦颖,王娇娇,卢毅. 认知无线传感器网络频谱分配的一种改进方法[J]. 西安电子科技大学学报, 2020, 47(3): 80-85.
[5]	杜振鑫,刘广钟,赵学华. 集成学习人工蜂群算法[J]. 西安电子科技大学学报, 2019, 46(2): 124-131.
[6]	王燕. 改进多目标进化算法的云工作流调度[J]. 西安电子科技大学学报, 2019, 46(1): 130-136.
[7]	宋嘉奇, 陶海红. 一种稀布阵列天线的近场波束综合算法[J]. 西安电子科技大学学报, 2018, 45(6): 14-18.
[8]	李枫, 王紫阳, 王乐, 李蕊, 冯罡, 尹应增. 抗干扰阵列天线的小型化及波束形成技术分析[J]. 西安电子科技大学学报, 2018, 45(6): 51-56.
[9]	林峰;陈建军;曹鸿钧. 复合材料压力容器的概率与区间可靠性设计[J]. 西安电子科技大学学报, 2017, 44(1): 45-51.
[10]	杨国平;刘三阳;张建科. 极小极大问题的生物地理学优化邻近点算法[J]. 西安电子科技大学学报, 2016, 43(5): 88-92+182.
[11]	雷雨;焦李成;公茂果;李玲玲. 求解多目标考试时间表问题的NNIA改进算法[J]. 西安电子科技大学学报, 2016, 43(2): 157-161+192.
[12]	温鸿航;葛建华;许唐雯. 一种低复杂度OFDM峰均比降低方法[J]. J4, 2015, 42(5): 188-193.
[13]	高军;周禹龙;杨群;曹祥玉 . 一种低副瓣锐截止波束赋形阵列天线设计 [J]. J4, 2015, 42(2): 152-156.
[14]	代才;王宇平;何晓光. 高维多目标问题的排序新方法[J]. J4, 2014, 41(6): 89-94.
[15]	王立霞;付小宁. 靶场钻地弹爆炸位置的探测[J]. J4, 2014, 41(5): 124-128+172.

求解约束优化问题M-精英协同进化算法

M-Elite coevolutionary algorithm for constrained optimization

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0