Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Inver-over Operator for the TSP
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A new MOEA for multi-objective TSP and Its convergence property analysis
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A hybrid heuristic for the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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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 front 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 front efficiently for larger size DMO-TSP with higher conflict degrees between objectives by distributed parallel computer systems with shared memory.