基于自適應搜索的免疫粒子群算法

張超; 李擎; 王偉乾; 陳鵬; 馮毅南

doi:10.13374/j.issn2095-9389.2017.01.016

基于自適應搜索的免疫粒子群算法

doi: 10.13374/j.issn2095-9389.2017.01.016

張超¹,
李擎^1, ,,
王偉乾²,
陳鵬¹,
馮毅南¹

1) 北京科技大學自動化學院, 北京 100083
2) 中國電子科技集團第二研究所, 太原 030024

基金項目:

國家自然科學基金青年基金資助項目（61603362，61603034）

詳細信息

中圖分類號: TP18
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出版歷程
- 收稿日期: 2016-01-29

Immune particle swarm optimization algorithm based on the adaptive search strategy

1) School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2) The Second Research Institute of China Electronics Technology Group Corporation, Taiyuan 030024, China

摘要

摘要: 經典粒子群算法由于多樣性差而陷入局部最優，從而造成早熟停滯現象.為克服上述缺點，本文結合人工免疫算法，提出一種基于自適應搜索的免疫粒子群算法.首先，該算法改善了濃度機制；然后由粒子最大濃度值來控制子種群數目以充分利用粒子種群資源；最后對劣質子種群進行疫苗接種，利用粒子最大濃度值調節接種疫苗的搜索范圍，不僅避免了種群退化現象，而且提高了算法的收斂精度和全局搜索能力.仿真結果表明該算法求解復雜函數優化問題的有效性和優越性.
- 粒子群算法 /
- 人工免疫算法 /
- 自適應搜索 /
- 海明距離
Abstract: The particle swarm algorithm is often trapped in a local optimum due to poor diversity, resulting in a premature stagnation phenomenon. In order to overcome this shortcoming, an immune particle swarm optimization algorithm based on the adaptive search strategy was proposed in this paper. Firstly, the concentration mechanism was improved. Secondly, in order to make full use of the resources of the particle population, the number of particles of sub-populations was controlled by the maximum concentration of particles. Finally, the inferior sub-populations were vaccinated, and the maximum concentration of particles was used to control the search range of the vaccine, so the population degradation was avoided, and the convergence accuracy and the global search ability of the algorithm were improved. Simulation results show the effectiveness and superiority of the proposed algorithm in solving the complex function optimization problems.
- particle swarm optimization /
- artificial immune algorithm /
- adaptive search /
- Hamming distance

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參考文獻(6)

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