混合均值中心反向学习粒子群优化算法

doi:10.3969/j.issn.0372-2112.2019.09.001

电子学报 ›› 2019, Vol. 47 ›› Issue (9): 1809-1818.DOI: 10.3969/j.issn.0372-2112.2019.09.001

所属专题：粒子群优化算法

• 学术论文 • 下一篇

混合均值中心反向学习粒子群优化算法

孙辉^1,2,3, 邓志诚¹, 赵嘉^1,2,3, 王晖^1,2,3, 谢海华¹

1. 南昌工程学院信息工程学院, 江西南昌 330099;
2. 鄱阳湖流域水工程安全与资源高效利用国家地方联合工程实验室, 江西南昌 330099;
3. 江西省水信息协同感知与智能处理重点实验室, 江西南昌 330099

收稿日期:2019-01-06 修回日期:2019-03-15 出版日期:2019-09-25
- 作者简介:
- 孙辉男,1959年3月生,江西九江人.博士、二级教授.主要研究方向为智能计算、Rough集和粒计算、变分不等原理与变分不等式.E-mail:sun_hui2006@163.com;邓志诚 男,1995年2月生,江西丰城人.硕士研究生.主要研究方向为智能计算;赵嘉男,1981年9月生,安徽桐城人.教授、硕士生导师、中国电子学会高级会员.主要研究方向为智能计算与大数据挖掘;王晖男,1982年8月生,湖北红安人.博士、教授、硕士生导师.主要研究方向为智能计算与并行计算;谢海华 男,1994年2月生,江西萍乡人.硕士研究生.主要研究方向为智能计算.
- 基金资助:
- 国家自然科学基金 (No.61663029，No.51669014，No.61663028）; 江西省杰出青年基金 (No.2018ACB21029）; 江西省杰出青年人才资助计划 (No.20171BCB23075）; 江西省自然科学基金 (No.20171BAB202035）; 江西省教育厅落地计划基金 (No.KJLD13096）; 江西省2018年度研究生创新专项资金项目 (No.YC2018-S422）

Hybrid Mean Center Opposition-Based Learning Particle Swarm Optimization

SUN Hui^1,2,3, DENG Zhi-cheng¹, ZHAO Jia^1,2,3, WANG Hui^1,2,3, XIE Hai-hua¹

1. School of Information Engineering, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, China;
2. National-Local Joint Engineering Laboratory of Water Engineering Safety and Effective Utilization of Resources in Poyang Lake Area, Nanchang, Jiangxi 330099, China;
3. Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, Jiangxi 330099, China

Received:2019-01-06 Revised:2019-03-15 Online:2019-09-25 Published:2019-09-25
- Supported by:
- National Natural Science Foundation of China (No.61663029, No.51669014, No.61663028); Outstanding Youth Fund of Jiangxi Province (No.2018ACB21029); Outstanding Youth Talents Project of Jiangxi Province (No.20171BCB23075); Natural Science Foundation of Jiangxi Province, China (No.20171BAB202035); Foundation of Education Department of Jiangxi Province (No.KJLD13096); Graduate Innovation Foundation of Jiangxi Province in 2018 (No.YC2018-S422)

摘要/Abstract

摘要： 为平衡粒子群算法勘探与开发能力，本文提出混合均值中心反向学习粒子群优化算法.算法将所有粒子和部分优质粒子分别构造的均值中心进行贪心选择，得出的混合均值中心将对粒子所在区域进行精细搜索.同时对混合均值中心进行反向学习，使粒子能探索更多新区域.将本文算法与最新改进的粒子群算法、人工蜂群算法和差分算法在多种测试函数集上进行比较，实验结果验证了混合均值中心反向学习策略的有效性，算法的综合优化性能更强.

关键词: 全局寻优, 混合均值中心, 反向学习, 粒子群优化算法

Abstract: In order to balance the exploration and exploitation of particle swarm optimization, this paper proposes a hybrid mean center opposition-based learning particle swarm optimization. The algorithm performs greedy selection on the mean center of all particles and some high-quality particles respectively, and the obtained hybrid mean center will search the region in detail where the particles are located. At the same time, the hybrid mean center is using opposition-based learning, so that the particles can explore more new regions. The proposed algorithm are compared with the latest improved particle swarm optimization, artificial bee colony algorithm and difference algorithm in various test function sets, and the results verify the effectiveness of the hybrid mean center opposition-based learning and the overall optimization performance of the algorithm is stronger.

Key words: global optimization, hybrid mean center, opposition-based learning, particle swarm optimization

中图分类号:

TP182
TP391

孙辉, 邓志诚, 赵嘉, 等. 混合均值中心反向学习粒子群优化算法[J]. 电子学报, 2019, 47(9): 1809-1818.

SUN Hui, DENG Zhi-cheng, ZHAO Jia, et al. Hybrid Mean Center Opposition-Based Learning Particle Swarm Optimization[J]. Acta Electronica Sinica, 2019, 47(9): 1809-1818.

参考文献

[1] KENNEDY J,EBERHART R.Particle swarm optimization[A].Proceedings of International Conference on Neural Networks[C].Piscataway:IEEE,1995.1942-1948.
[2] 李奕,吴小俊.粒子群进化学习自适应双通道脉冲耦合神经网络图像融合方法研究[J].电子学报,2014,42(2):217-222. LI Yi,WU Xiao-Jun.A novel image fusion method using self-adaptive dual-channel pulse coupled neural networks based on PSO evolutionary learning[J].Acta Electronica Sinica,2014,42(2):217-222.(in Chinese)
[3] 俞海珍,汪鹏君,张会红,万凯.基于三值多样性粒子群算法的MPRM电路综合优化[J].电子学报,2017,45(7):1601-1607. YU Hai-zhen,WANG Peng-jun,ZHAO Hui-hong,et al.Optimization of MPRM circuits based on ternary diversity particle swarm optimization[J].Acta Electronica Sinica,2017,45(7):1601-1607.(in Chinese)
[4] ZHAO J,LV L,WANG H,et al.Particle swarm optimization based on vector Gaussian learning[J].Ksii Transactions on Internet and Information Systems,2017,11(4):2038-2057.
[5] HAKLl H,UGUZ H.A novel particle swarm optimization algorithm with Levy flight[J].Applied Soft Computing,2014,23:333-345.
[6] YAN B,ZHAO Z,ZHOU Y,et al.A particle swarm optimization algorithm with random learning mechanism and Levy flight for optimization of atomic clusters[J].Computer Physics Communications,2017,219:79-86.
[7] CHEN K,ZHOU F,YIN L,et al.A hybrid particle swarm optimizer with sine cosine acceleration coefficients[J].Information Sciences,2018,422:218-241.
[8] 朱蓉,靳雁霞,范卫华.融合优质粒子分布的粒子群优化算法[J].小型微型计算机系统,2015,36(3):576-580. ZHU Rong,JIN Yan-xia,FAN Wei-hua.Particle swarm optimization algorithm combination with the distribution of superior quality particles[J].Journal of Chinese Computer Systems,2015,36(3):576-580.(in Chinese)
[9] WANG H,WU Z,RAHNAMAYAN S,et al.Enhancing particle swarm optimization using generalized opposition-based learning[J].Information Sciences,2011,181(20):4699-4714.
[10] WANG H,WU Z,LIU Y,et al.Space transformation search:a new evolutionary technique[A].Proceedings of Word Summit Genetic Evolutionary Computation[C].New York:ACM,2009.537-544.
[11] CHEN Q,LIU B,ZHANG Q,et al.Problem Definitions and Evaluation Criteria for CEC 2015 Special Session and Competition on Bound Constrained Single-Objective Computationally Expensive Numerical Optimization[R].Zhengzhou:Zhengzhou University,2014.
[12] JENSI R,JIJI G W.An enhanced particle swarm optimization with levy flight for global optimization[J].Applied Soft Computing,2016,43:248-261.
[13] OUYANG H,GAO L,LI S,et al.Improved global-best-guided particle swarm optimization with learning operation for global optimization problems[J].Applied Soft Computing,2017,52:987-1008.
[14] 周凌云,丁立新,彭虎,等.一种邻域重心反向学习的粒子群优化算法[J].电子学报,2017,45(11):2815-2824. ZHOU Ling-yun,DING Li-xin,PENG Hu,et al.Neighborhood centroid opposition-based particle swarm optimization[J].Acta Electronica Sinica,2017,45(11):2815-2824.(in Chinese)
[15] LIANG J J,QU B Y,SUGANTHAN P N,et al.Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization[R].Zhengzhou:Zhengzhou University,2013.
[16] 孙辉,谢海华,赵嘉.正弦选择概率模型的全局最优引导人工蜂群算法[J].南昌工程学院学报,2018,37(6):84-90. SUN Hui,XIE Hia-hua,ZHAO Jia.Global optical guided artificial bee colony algorithm based on sinusoidal selection probability model[J].Journal of Nanchang Institute of Technology,2018,37(6):84-90.(in Chinese)
[17] CUI L,LI G,LIN Q,et al.A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation[J].Information Sciences,2016,367:1012-1044.
[18] AYDILEK B.A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems[J].Applied Soft Computing,2018,66:232-249

混合均值中心反向学习粒子群优化算法

Hybrid Mean Center Opposition-Based Learning Particle Swarm Optimization

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