Firefly algorithm based on intelligent single particle learning. (15th January 2021)

• Record Type: Journal Article
• Authors: Chen, Wenping; Ye, Jun; Wu, Runxiu; Liu, Guangming; Kang, Ping
• Is Part Of: International journal of computing science and mathematics. Volume 12:Number 4(2020)
• Page Start: 309
• Page End: 326
• Publication Date: 2021-01-15
• Subjects: firefly algorithm -- intelligent single particle optimisation -- all-attractive model -- detection and development -- global optimal particle -- self-learning; Mathematics -- Periodicals; Computer science -- Periodicals; Mathematics -- Data processing -- Periodicals; 510.285
• Languages: English
• ISSNs: 1752-5055

Abstract

The particles in the population of the firefly algorithm learn from each other using an all-attractive model, and the algorithm has a strong ability of social learning and global detection. However, the algorithm ignores the role of the global optimal particle, resulting in weak self-learning and local development ability of the algorithm. Therefore, this paper proposes an intelligent single-particle learning firefly algorithm. The algorithm divides the iterative process into two stages, the first stage adopts the standard firefly algorithm to evolve; in the second stage, the intelligent single particle optimisation algorithm is used to optimise the global optimal particle. The iterative process in the first stage ensures the sociality and global detection ability of the particle, and the second stage enhances the ability of self-learning and local development of the algorithm. The experimental results show that the algorithm in this paper has better performance.