  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.
