A sophisticated PSO based on multi-level adaptation and purposeful detection
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Although particle swarm optimization (PSO) has successfully applied on many global optimization problems, it is prone to premature convergence due to its monotonic and static learning pattern for all individuals. Furthermore, few purposeful operator is proposed to help population jump out of potential local optimum. To address the drawbacks and improve the comprehensive performance of PSO, we propose a sophisticated PSO (SopPSO) based on multi-level adaptation and purposeful detection. In SopPSO, a particle not only updates its learning model according to its fitness landscape, but also periodically re-selects target dimensions that the particle learns from its neighbors. The adaptive strategy applied in multi-level (i.e., individual level and dimension level) endows PSO with a more accurate simulation on emergent collective behaviors. In addition, a tabu detecting and a local searching strategies based on some historical information are proposed to help the population to jump out of local optima and improve the accuracy of solutions, respectively. The extensive experimental results illustrate the effectiveness and efficiency of the proposed strategies. Furthermore, the comparison results between SopPSO and other peer algorithms on different problems verify its favorable performance on unimodal, multimodal and large-scale problems as well as some real applications.
KeywordsParticle swarm optimization Global optimization Multi-level adaptation Tabu detecting strategy Local learning strategy
This studywas funded by the NationalNatural Science Foundation of China (Nos. 61663009, 61602174, 61562028), the National Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202064, 20161BAB212052, 20151BAB207022) and the National Natural Science Foundation of Jiangxi Provincial Department of Education (Nos. GJJ160469, GJJ150496).
Compliance with ethical standards
Conflict of interest
The authors claim that none of the material in the paper has been published or is under consideration for publication elsewhere. And all authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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