A Guo-tao-algorithm-based Non-dominated Sorting Approach to Multiobjective Optimization


Guo Tao (GT) algorithm is a simple but powerful evolutionary optimization algorithm with many successful applications. For its simplicity and efficiency in single objective optimization, it can be easily extended to handing MOPs. In this paper we propose a Non-dominated Sorting Guo Tao algorithm (NSGT) on multi-objective optimization. NSGT combines the advantages of GT algorithm with the mechanisms of Pareto based ranking and crowding distance sorting, used by state-of-the-art evolutionary algorithms for multi-objective optimization. NSGT is tested on five ZDT functions and achieves competitive results.