Cat swarm optimization with normal mutation for fast convergence of multimodal functions

A normal mutation strategy based cat swarm optimization (NMCSO) that features effective global search capabilities with accelerating convergence speed is presented. The classical CSO suffers from the premature convergence and gets easily trapped in the local optima because of the random mutation process. This frailty has restricted wider range of applications of the classical CSO. To overcome the drawbacks, the normal mutation is adopted in the mutation process of this paper. It enables the cats to seek the positions in better directions by avoiding the problem of premature convergence and local optima. Experiments are conducted on several benchmark unimodal, rotated, unrotated and shifted multimodal problems to demonstrate the effectiveness of the proposed method. Furthermore, NMCSO is also applied to solve the large parameter optimization problems. The experimental results illustrate that the proposed method is quite superior to classical CSO, particle swarm optimization (PSO) and some of the state of the art evolutionary algorithms in terms of convergence speed, global optimality, solution accuracy and algorithm reliability. (C) 2018 Elsevier B.V. All rights reserved.