A non-random multi-objective cat swarm optimization algorithm based on CAT MAP
Chongfan Luo, Ya'Nan Guo, Yide Ma, Chao Lv, Yan Zhang · 2016
In this paper, a new multi-objective cat swarm optimization algorithm has been proposed. The algorithm applies the part of individuals into the seeking mode and the other part of individuals into the tracing mode non-randomly. Cat map is used to initialize individuals of population. In this way, individuals can avoid trapping into local optimal in the final iteration process and the search ability of algorithm can be improved effectively. The performance of proposed method is testified by using 4 multi-objective test functions. A quantitative assessment of proposed method is carried out using several performances metrics and compared with the previous design methods NSGA-II and MOPSO. The experiments illustrate that the proposed algorithm is better than the other algorithms.