A Parallel Multi-algorithm Solver for Dynamic Multi-Objective TSP (DMO-TSP)

Dynamic Multi-Objective TSP (DMO-TSP) proposed as a theoretical model of mobile communication network in 2004 is an NP-hard problem. The problem dynamically changes the characteristics of its objectives, the conflict degrees between its objectives and the number of its cities. In fact, a Dynamic Multi-Objective TSP is not a single optimization problem, but a diverse set of optimization problems. The No Free Lunch Theorems in optimization and numerical experiments have demonstrated that it is impossible to develop a single evolutionary algorithm for population evolution that is always efficient and effective for solving such an extremely complicated diverse set of optimization problems. In this paper, a parallelized form of the multi-algorithm co-evolution strategy (MACS) for DMO-TSP called synchronized parallel multi-algorithm solver is proposed, because the MACS solver can just continuously track the moving Pareto of small size(about 100 cities) DMO-TSP with two objectives in lower degree of conflict. It is hoped that the synchronized parallel multi-algorithm solver can be used to track the moving Pareto set efficiently for larger size DMO-TSP with higher conflict degrees between objectives by distributed parallel computer systems with shared memory.