The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
A Dynamic Neighborhood-Based Switching Particle Swarm Optimization Algorithm
Open accessAbstract
In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evolution information among the entire swarm. In addition, a novel switching learning strategy is introduced to adaptively select the acceleration coefficients and update the velocity model according to the searching state at each iteration, thereby contributing to a thorough search of the problem space. Furthermore, the differential evolution algorithm is successfully hybridized with the particle swarm optimization (PSO) algorithm to alleviate premature convergence. A series of commonly used benchmark functions (including unimodal, multimodal, and rotated multimodal cases) is utilized to comprehensively evaluate the performance of the DNSPSO algorithm. The experimental results demonstrate that the developed DNSPSO algorithm outperforms a number of existing PSO algorithms in terms of the solution accuracy and convergence performance, especially for complicated multimodal optimization problems.
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Cites
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A Cooperative Approach to Particle Swarm Optimization
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Adaptive Particle Swarm Optimization
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Metadata
- Authors
- Zeng, N., Wang, Z., Liu, W., Zhang, H., Hone, K., & Liu, X.
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2022-09
- Volume
- 52
- Issue
- 9
- Pages
- 9290-9301
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W3101667383
- Cited by
- 269
- References
- 41
- FWCI
- 13.26
- Type
- journal article
- Language
- en
- Open access
- green
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