1. 多策略融合改进的自适应被囊群算法.
- Author
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柴岩, 李广友, 任生, and 许兆楠
- Subjects
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MARKOV processes , *POINT set theory , *PROBLEM solving , *TUNICATA , *COMPUTER simulation - Abstract
In order to solve the problems of inadequate global search and easy to fall into local extremum, this paper proposed a multi-strategy fusion improved adaptive tunicate swarm algorithm(MITSA). Firstly, this paper used the best point set strategy to improve population diversity during initialization. Secondly, it offered a multi-elite cooperative guidance mechanism to optimize the tunicates’ location information, and enhance the global exploration ability of the algorithm by increasing the exploration possibility of unknown search area. Then, the algorithm achieved dynamic global and local search balance by introducing adaptive weight factors into the group behavior stage. Meanwhile, probabilistic wavelet variation strategy promoted individual dynamic fine-tuning to enhance the anti-stagnation ability of the algorithm, while using the greedy principle to retain excellent information to help the population to the food source. Finally, this paper proved the global convergence of the improved algorithm based on Markov chain theory. Through the numerical simulation of the benchmark test function and CEC2014 complex function, the experimental results and Wilcoxon rank sum test results comprehensively verify MITSA’s excellent convergence accuracy, robust robustness and high-dimensional scalability. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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