Immune particle swarm optimization algorithm based on the adaptive search strategy

ZHANG Chao; LI Qing; WANG Wei-qian; CHEN Peng; FENG Yi-nan

doi:10.13374/j.issn2095-9389.2017.01.016

Volume 39 Issue 1

Jan. 2017

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ZHANG Chao, LI Qing, WANG Wei-qian, CHEN Peng, FENG Yi-nan. Immune particle swarm optimization algorithm based on the adaptive search strategy[J]. Chinese Journal of Engineering, 2017, 39(1): 125-132. doi: 10.13374/j.issn2095-9389.2017.01.016

Citation:

ZHANG Chao, LI Qing, WANG Wei-qian, CHEN Peng, FENG Yi-nan. Immune particle swarm optimization algorithm based on the adaptive search strategy[J]. Chinese Journal of Engineering, 2017, 39(1): 125-132. doi: 10.13374/j.issn2095-9389.2017.01.016

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Immune particle swarm optimization algorithm based on the adaptive search strategy

doi: 10.13374/j.issn2095-9389.2017.01.016

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

Received Date: 2016-01-29

Abstract

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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References(6)

References

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