Cited By
View all- Chauhan DShivani Cheng R(2024)Competitive Swarm Optimizer: A decade surveySwarm and Evolutionary Computation10.1016/j.swevo.2024.10154387(101543)Online publication date: Jun-2024
Enhanced Competitive Swarm Optimizer for Multi-task Optimization
Abstract
In the real world, many problems possess the high degree of similarity. Inspired by multifactorial inheritance model, evolutionary multi-task optimization paradigm is proposed to simultaneously solve multiple optimization problems. However, experimental results have revealed that the performance of multifactorial evolutionary algorithm deteriorates with negative knowledge transfer between uncorrelated tasks. To alleviate this issue, we proposed an enhanced competitive swarm optimizer to explore the generality of the multitasking paradigm. A new velocity update mechanism for losers is proposed to improve the search ability. Further, a mating approach is proposed to transfer implicit knowledge among tasks for generating offspring. Experimental and statistical analyses are performed on CEC2017 evolutionary multitask optimization competition. Results show that the proposed algorithm is competitive in comparison with other state-of-the-art multifactorial optimization algorithms.
References
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Index Terms
- Enhanced Competitive Swarm Optimizer for Multi-task Optimization
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Published In
October 2020
552 pages
ISBN:9781450387835
DOI:10.1145/3436369
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- Beijing University of Technology
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 January 2021
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ICCPR 2020
ICCPR 2020: 2020 9th International Conference on Computing and Pattern Recognition
October 30 - November 1, 2020
Xiamen, China
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View all- Chauhan DShivani Cheng R(2024)Competitive Swarm Optimizer: A decade surveySwarm and Evolutionary Computation10.1016/j.swevo.2024.10154387(101543)Online publication date: Jun-2024
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