A multi-phase adaptively guided multiobjective evolutionary algorithm based on decomposition for travelling salesman problem

In this paper, a multi-phase strategy for dynamic resource allocation is proposed for some special optimization problems where the evolutionary process cannot be explicitly divided into two phases, under the decomposition-based multiobjective evolutionary optimization framework. Based on the evolutionary status, a switching mechanism is adopted to adaptively use either convergence or diversity information in the external archive, to guide the evolutionary search in the working population. The proposed algorithm is compared with six well-known multiobjective evolutionary algorithms on multiobjective travelling salesman problem (MOTSP). Experimental results show that our proposed algorithm performs better than other compared algorithms.