[1]钱小宇,葛洪伟,蔡明.基于目标空间分解和连续变异的多目标粒子群算法[J].智能系统学报,2019,14(03):464-470.[doi:10.11992/tis.201711015]
　QIAN Xiaoyu,GE Hongwei,CAI Ming.Decomposition and continuous mutation-based multi-objective particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2019,14(03):464-470.[doi:10.11992/tis.201711015]
点击复制

基于目标空间分解和连续变异的多目标粒子群算法(/HTML)
分享到：

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年03期
页码:
464-470
栏目:
出版日期:
2019-05-05

文章信息/Info

Title:
Decomposition and continuous mutation-based multi-objective particle swarm optimization
作者:
钱小宇1; 2;  葛洪伟1; 2;  蔡明3
1. 江南大学 轻工过程先进控制教育部重点实验室, 江苏 无锡 214122;
2. 江南大学 物联网工程学院, 江苏 无锡 214122;
3. 江南大学 信息化建设与管理中心, 江苏 无锡 214122
Author(s):
QIAN Xiaoyu1; 2;  GE Hongwei1; 2;  CAI Ming3
1. Ministry of Education Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China;
2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
3. Information Construction and Ma
关键词:
多目标优化; 粒子群优化算法; 分解; 子区域; 变异; 差分; 高斯变异; 柯西变异
Keywords:
multi-objective optimization; particle swarm optimization algorithm; decomposition; sub-region; mutation; differential; Gaussian mutation; Cauchy mutation
分类号:
TP391.4
DOI:
10.11992/tis.201711015
摘要:
针对当前多目标粒子群优化算法收敛性和多样性不佳等问题，提出了一种基于目标空间分解和连续变异的多目标粒子群优化算法。利用目标空间分解方法将粒子群分配到预先设定好的子区域中，在该过程中，通过一种新适应值公式来对每个子区域中的粒子进行择优筛选，该适应值公式融入了支配强度因素；在全局搜索过程中，使用差分变异、高斯变异和柯西变异对全局引导粒子的位置进行连续变异操作。将该算法与当前主流的一些多目标优化算法进行对比实验，结果表明，本文提出的算法在提高粒子收敛性的同时，多样性也得到了提升。
Abstract:
In light of the poor convergence problems and the diversity of current multi-objective optimization algorithms, in this paper, we propose an objective-space decomposition and continuous mutation-based multi-objective particle-swarm-optimization algorithm. Its innovations are as follows:we use a space decomposition method to distribute the particle swarm into a predefined sub-region. During this process, we apply a new adaptive value formula to select and filter the particles in each sub-region and incorporate a fitness formula into the dominance factor. In the global search process, we apply differential, Gaussian, and Cauchy mutations to continuously mutate the position of the global guide particle. We compare the performance of this algorithm with those of current multi-objective optimization algorithms, and the results show that the proposed algorithm improves the convergence and diversity of the particles.

参考文献/References:

[1] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of ICNN’95-International Conference on Neural Networks. Perth, WA, Australia, Australia:IEEE, 1995:1942-1948.
[2] COELLO C A C, LECHUGA M S. MOPSO:a proposal for multiple objective particle swarm optimization[C]//Proceedings of the 2002 Congress on Evolutionary Computation. Honolulu, HI, USA:IEEE, 2002:1051-1056.
[3] COELLO C A C, PULIDO G T, LECHUGA M S. Handling multiple objectives with particle swarm optimization[J]. IEEE transactions on evolutionary computation, 2004, 8(3):256-279.
[4] RAQUEL C R, NAVAL P C JR. An effective use of crowding distance in multiobjective particle swarm optimization[C]//Proceedings of the 7th Annual Conference Genetic and Evolutionary Computation Washington, DC, USA:ACM, 2005:257-264.
[5] LI Li, WANG Wanliang, XU Xinli. Multi-objective Particle swarm optimization based on global margin ranking[J]. Information sciences, 2017, 375:30-47.
[6] LIN Qiuzhen, LI Jiangqiang, DU Zhihua, et al. A novel multi-objective particle swarm optimization with multiple search strategies[J]. European journal of operational research, 2015, 247(3):732-744.
[7] DAI Cai, WANG Yuping, YE Miao. A new multi-objective particle swarm optimization algorithm based on decomposition[J]. Information sciences, 2015, 325:541-557.
[8] CHENG Tingli, CHEN Minyou, FLEMING P J, et al. A novel hybrid teaching learning based multi-objective particle swarm optimization[J]. Neurocomputing, 2017, 222:11-25.
[9] SU Yixin, CHI Rui. Multi-objective particle swarm-differential evolution algorithm[J]. Neural Computing and applications, 2017, 28(2):407-418.
[10] ZITZLER E, LAUMANNS M, THIELE L. SPEA2:Improving the strength Pareto evolutionary algorithm for multiobjective optimization[M]//GIANNAKOGLOU K C, TSAHALIS D T, PéRIAUX J, et al. Evolutionary Methods for Design, Optimisation and Control with Applications to Industrial Problems. Athens, Greece:International Center for Numerical Methods in Engineering, 2002:95-100.
[11] JORDEHI A R. Enhanced leader PSO (ELPSO):a new PSO variant for solving global optimisation problems[J]. Applied Soft Computing, 2015, 26:401-417.
[12] 陈明杰, 黄佰川, 张旻. 混合改进蚁群算法的函数优化[J]. 智能系统学报, 2012, 7(4):370-376 CHEN Mingjie, HUANG Baichuan, ZHANG Min. Function optimization based on an improved hybrid ACO[J]. CAAI transactions on intelligent systems, 2012, 7(4):370-376
[13] CHELLAPILLA K, FOGEL D B. Two new mutation operators for enhanced search and optimization in evolutionary programming[C]//Proceedings Volume 3165, Applications of Soft Computing. San Diego, CA, United States:SPIE, 1997:260-269.
[14] GONG Maoguo, JIAO Licheng, DU Haifeng, et al. Multiobjective immune algorithm with nondominated neighbor-based selection[J]. Evolutionary computation, 2008, 16(2):225-255.
[15] STORN R, PRICE K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of global optimization, 1997, 11(4):341-359.
[16] ZITZLER E, THIELE L, LAUMANNS M, et al. Performance assessment of multiobjective optimizers:an analysis and review[J]. IEEE transactions on evolutionary computation, 2003, 7(2):117-132.

相似文献/References:

[1]蒋建国,吴 琼,夏 娜.自适应粒子群算法求解Agent联盟[J].智能系统学报,2007,2(02):69.
　JIANG Jian-guo,WU Qiong,XIA Na.Solving Agent coalition using adaptive particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2007,2(03):69.
[2]王兆伟,肖 扬,刘湘黔.基于粒子群算法的MIMO CDMA平坦衰落信道均衡器[J].智能系统学报,2008,3(01):38.
　WANG Zhao-wei,XIAO Yang,LIU Xiang-qian.Application of particle swarm optimization in MIMO CDMA flat fading channel equalizers[J].CAAI Transactions on Intelligent Systems,2008,3(03):38.
[3]薛英花,田国会,吴 皓,等.智能空间中的服务机器人路径规划[J].智能系统学报,2010,5(03):260.
　XUE Ying-hua,TIAN Guo-hui,WU Hao,et al.Path planning for service robots in an intelligent space[J].CAAI Transactions on Intelligent Systems,2010,5(03):260.
[4]王 艳,曾建潮.多目标微粒群优化算法综述[J].智能系统学报,2010,5(05):377.[doi:10.3969/j.issn.1673-4785.2010.05.001]
　WANG Yan,ZENG Jian-chao.A survey of a multiobjective particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2010,5(03):377.[doi:10.3969/j.issn.1673-4785.2010.05.001]
[5]马胜蓝,叶东毅.一种带禁忌搜索的粒子并行子群最小约简算法[J].智能系统学报,2011,6(02):132.
　MA Shenglan,YE Dongyi.A minimum reduction algorithm based on parallel particle subswarm optimization with tabu search capability[J].CAAI Transactions on Intelligent Systems,2011,6(03):132.
[6]秦全德,李丽,程适,等.交互学习的粒子群优化算法[J].智能系统学报,2012,7(06):547.
　QIN Quande,LI Li,CHENG Shi,et al.Interactive learning particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2012,7(03):547.
[7]陶新民,徐鹏,刘福荣,等.组合分布估计和差分进化的多目标优化算法[J].智能系统学报,2013,8(01):39.[doi:10.3969/j.issn.1673-4785.201208035]
　TAO Xinmin,XU Peng,LIU Furong,et al.Multi objective optimization algorithm composed of estimation of distribution and differential evolution[J].CAAI Transactions on Intelligent Systems,2013,8(03):39.[doi:10.3969/j.issn.1673-4785.201208035]
[8]刘长平,叶春明.具有Lévy飞行特征的蝙蝠算法[J].智能系统学报,2013,8(03):240.
　LIU Changping,YE Chunming.Bat algorithm with the characteristics of Lévy flights[J].CAAI Transactions on Intelligent Systems,2013,8(03):240.
[9]孙文新,穆华平.自适应群体结构的粒子群优化算法[J].智能系统学报,2013,8(04):372.[doi:10.3969/j.issn.1673-4785.201211041]
　SUN Wenxin,MU Huaping.Particle swarm optimization based on self-adaptive population structure[J].CAAI Transactions on Intelligent Systems,2013,8(03):372.[doi:10.3969/j.issn.1673-4785.201211041]
[10]张俊玲,陈增强,张青.基于粒子群优化的Elman神经网络无模型控制[J].智能系统学报,2016,11(1):49.[doi:10.11992/tis.201507025]
　ZHANG Junling,CHEN Zengqiang,ZHANG Qing.Elman model-free control method based on particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2016,11(03):49.[doi:10.11992/tis.201507025]

备注/Memo

备注/Memo:
收稿日期:2017-11-13。
基金项目:江苏省普通高校研究生科研创新计划项目（KYLX16_0781，KYLX16_0782）；江苏高校优势学科建设工程资助项目（PAPD）.
作者简介:钱小宇,男,1992年生,硕士研究生,主要研究方向为人工智能与模式识别;葛洪伟,男,1967年生,教授,博士,博士生导师,主要研究方向为人工智能与模式识别、机器学习、图像处理与分析等;蔡明,男,1962年生,高级工程师,主要研究方向为计算机软件、网络应用的研究。
通讯作者:葛洪伟.E-mail:ghw8601@163.com
更新日期/Last Update: 1900-01-01