Dynamic Cat Swarm Optimization Algorithm for Backboard Wiring Problem

TL;DR

This paper introduces a modified dynamic cat swarm optimization algorithm that effectively balances exploration and exploitation, outperforming existing algorithms in solving complex optimization problems including real-world scenarios.

Contribution

The paper proposes a novel modification to the Cat Swarm Optimization algorithm to prevent premature convergence by balancing exploration and exploitation phases.

Findings

Ranks first among several well-known algorithms in tests

Effectively balances exploration and exploitation

Proven superior performance on classical, modern, and real-world problems

Abstract

This paper presents a powerful swarm intelligence meta-heuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization. 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 as well as future directions to further improve the algorithm are discussed.