Multi-Algorithm Co-evolution Strategy for Dynamic Multi-Objective TSP

Dynamic Multi-Objective TSP (DMOTSP), a new research filed of evolutionary computation, is an NP-hard problem which comes from the applications of mobile computing and mobile communications. Because the characters of DMOTSP change with time, the method of designing a single algorithm can not effectively solve this extremely complicated and diverse optimization problem according to NFLTs for optimization. In this paper, a new approach to designing algorithm, multi-algorithm co-evolution strategy (MACS), for DMOTSP is proposed. Through multi-algorithm co-evolution, MACS can accelerate algorithm's convergence, make Pareto set maintain diversity and make Pareto front distribute evenly with a complementary performance of these algorithms and avoiding the limitations of a single algorithm. In experiment, taking the three-dimensional benchmark problem CHN144+5 with two-objective for example, the results show that MACS can solve DMOTSP effectively with faster convergence, better diversity of Pareto set and more even distribution of Pareto front than single algorithm.