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 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.

[1]  玉置 久,et al.  進化的アルゴリズムの方法論(〈特集〉進化的アルゴリズムとファジィ理論) , 1998 .

[2]  Ming Yang,et al.  Multi-algorithm co-evolution strategy for Dynamic Multi-Objective TSP , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[3]  Lishan Kang,et al.  A New MOEA for Multi-objective TSP and Its Convergence Property Analysis , 2003, EMO.

[4]  Keld Helsgaun,et al.  An effective implementation of the Lin-Kernighan traveling salesman heuristic , 2000, Eur. J. Oper. Res..

[5]  José Ignacio Hidalgo,et al.  A hybrid heuristic for the traveling salesman problem , 2001, IEEE Trans. Evol. Comput..

[6]  Athanasios V. Vasilakos,et al.  Evolutionary fuzzy multi-objective routing for wireless mobile ad hoc networks , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[7]  John J. Grefenstette,et al.  Proceedings of the 1st International Conference on Genetic Algorithms , 1985 .

[8]  Charles Gide,et al.  Cours d'économie politique , 1911 .

[9]  Chen Yu-ping,et al.  A gene-pool based genetic algorithm for TSP , 2008, Wuhan University Journal of Natural Sciences.

[10]  Leen Stougie,et al.  Competitive Algorithms for the On-line Traveling Salesman , 1995, WADS.

[11]  Hartmut Schmeck,et al.  An Ant Colony Optimization approach to dynamic TSP , 2001 .

[12]  Leen Stougie,et al.  Algorithms for the On-Line Travelling Salesman1 , 2001, Algorithmica.

[13]  Aimin Zhou,et al.  Benchmarking algorithms for dynamic travelling salesman problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[14]  B. Freisleben,et al.  Genetic local search for the TSP: new results , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[15]  Zbigniew Michalewicz,et al.  Inver-over Operator for the TSP , 1998, PPSN.

[16]  Rolf Drechsler,et al.  Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings , 2008, EvoWorkshops.

[17]  Bruce L. Golden,et al.  VEHICLE ROUTING: METHODS AND STUDIES , 1988 .

[18]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[19]  Martin Middendorf,et al.  Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP , 2001, EvoWorkshops.

[20]  R. Jonker,et al.  The symmetric traveling salesman problem and edge exchanges in minimal 1-trees , 1983 .

[21]  David W. Corne,et al.  Techniques for highly multiobjective optimisation: some nondominated points are better than others , 2007, GECCO '07.

[22]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[23]  David E. Goldberg,et al.  AllelesLociand the Traveling Salesman Problem , 1985, ICGA.

[24]  Zhaowang Ji,et al.  Finding multi-objective paths in stochastic networks: a simulation-based genetic algorithm approach , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[25]  James Kennedy,et al.  Proceedings of the 1998 IEEE International Conference on Evolutionary Computation [Book Review] , 1999, IEEE Transactions on Evolutionary Computation.

[26]  Richard Durbin,et al.  An analogue approach to the travelling salesman problem using an elastic net method , 1987, Nature.

[27]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

[28]  Jasper A Vrugt,et al.  Improved evolutionary optimization from genetically adaptive multimethod search , 2007, Proceedings of the National Academy of Sciences.