Slap swarm algorithm with memory mechanism and boundary collision processing

• Published in: Journal of physics. Conference series Vol. 2005; no. 1; pp. 12056 - 12064
• Main Authors: Yu, Kaixuan, Li, Yachao, Zhang, Dongsheng
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.08.2021
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2005/1/012056

Abstract Aiming at the problem of that the standard salp swarm algorithm has low result precision and slow convergence velocity in the evolutionary process, an improved salp swarm algorithm optimization based on memory mechanism and boundary collision processing is proposed. Firstly, a Chebyshev chaotic map was used to initialize the salps to make them distribute more evenly in search space. Secondly, adding the memory mechanism introduces the individual history optimum of salps into the optimizing strategy of individual salps, which accelerates the convergence speed of the algorithm. Finally, the boundary collision rebound mechanism is introduced to ensure the effectiveness and diversity of the population. The improved algorithm is simulated on 12 types of benchmark test functions in this paper, and compared with other intelligent optimization algorithms under the same conditions. The results show that the improved algorithm has not only a obvious improvement in convergence speed and convergence accuracy, but also has better optimization performance.