Hybrid Mean Center Opposition-Based Learning Particle Swarm Optimization

SUN Hui; DENG Zhi-cheng; ZHAO Jia; WANG Hui; XIE Hai-hua

doi:10.3969/j.issn.0372-2112.2019.09.001

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Hybrid Mean Center Opposition-Based Learning Particle Swarm Optimization

更新时间：2025-07-16

- Hybrid Mean Center Opposition-Based Learning Particle Swarm Optimization
- Acta Electronica Sinica Vol. 47, Issue 9, Pages: 1809-1818(2019)
- 作者机构：
  
  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
  4. 南昌工程学院信息工程学院,江西,南昌,330099
  5. 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
  6. 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
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.09.001
  CLC： TP182TP391
- Published Online：25 September 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
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.
DOI：

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. DOI： 10.3969/j.issn.0372-2112.2019.09.001.

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.

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.

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