Dynamic Cat Swarm Optimization algorithm for backboard wiring problem

• Published in: Neural computing & applications Vol. 33; no. 20; pp. 13981 - 13997
• Main Authors: Ahmed, Aram M., Rashid, Tarik A., Saeed, Soran Ab. M.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Entrapment; Exploitation; Heuristic methods; Image Processing and Computer Vision; Optimization; Optimization algorithms; Original Article; Phases; Probability and Statistics in Computer Science; Statistical methods; Swarm intelligence; Wiring; Exploration and exploitation phases; Cat Swarm Optimization; Dynamic Cat Swarm Optimization; Metaheuristics; Optimization
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06041-3

This paper presents a powerful swarm intelligence metaheuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization Algorithm. The original Cat Swarm Optimization suffers from the shortcoming of “premature convergence,” which is the possibility of entrapment in local optima which usually happens due to the off balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real-world scenario are used. In addition, the dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion and future directions to further improve the algorithm are discussed.