Abstract—Cat swarm optimization (CSO), a relatively new
swarm intelligence algorithm, exhibits better performance on
optimization problems than particle swarm optimization (PSO)
and weighted-PSO. This paper presents a variation on the
standard CSO algorithm called a vibrational mutation cat
swarm optimization, or VMCSO in order to efficiently increase
diversity of the swarm in the global searches. Comparing the
new algorithm with CSO and several CSO main variants
demonstrates the superiority of the VMCSO for the
Index Terms—Cat Swarm Optimization, Vibrational mutation, Diversity, Swarm intelligence.
Yan Zhang and Yide Ma are with the School of Information Science and Engineering, Lanzhou University, Lanzhou, China (corresponding author : Yide Ma; e-mail: zhangaoliao@ gmail.com, yidema@ gmail.com).
Cite: Yan Zhang and Yide Ma, "Cat Swarm Optimization with a Vibrational Mutation Strategy," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 510-515, 2014.