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Modelling and Implementation of Complex Systems pp 91–106Cite as

Enhanced Chemical Reaction Optimization for Multi-objective Traveling Salesman Problem

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1))

Abstract

The multi-objective traveling salesman problem (MOTSP) is an essential and challenging topic in the domains of engineering and optimization problems. In this paper we propose new variant of multi-objective chemical reaction optimization (MOCRO) called Enhanced MOCRO (EMOCRO) for solving MOTSP. The key idea of the proposed variant is the use of the dominance strategy and chemical reaction concepts. Compared to MOCRO, EMOCRO has a reduced number of parameters and a simplified general scheme. In order to give the quality of the algorithm, several MOTSP instances taken from the TSP library are used. The proposed approach is statistically compared with MOCRO and NSGA2. Results indicate that the EMOCRO outperformed other approaches in most of the test instances.

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References

  1. Lust, T., Teghem J.: The multiobjective traveling salesman problem: a survey and new approach. In: Nature Inspired Computing. vol. 272, pp. 119–137. Springer, Berlin (2010)

    Google Scholar 

  2. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. John Wiley and Sons Ltd. (2001)

    Google Scholar 

  3. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)

    Article  Google Scholar 

  4. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical report (2001)

    Google Scholar 

  5. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, USA (1989)

    Google Scholar 

  6. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  7. Lam, A., Li, V.: Chemical-reaction-inspired meta-heuristic for optimization. IEEE Trans. Evol. Comput. 14, 381–399 (2010)

    Article  Google Scholar 

  8. Fisher, R., Richter, K.: Solving a multiobjective traveling salesman problem by dynamic programming. Mathematische operations for schung und statistik, Series Optimization 13, 247–252 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  9. Paquete, L., Stutzle, T.: Design and analysis of stochastic local search for the multiobjective traveling salesman problem. Comput. Oper. Res. 36, 2619–2631 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  10. Hansen, M.P.: Use of substitute scalarizing functions to guide a local search based heuristic: the case of moTSP. J. Heuristics 6, 419–431 (2000)

    Article  MATH  Google Scholar 

  11. Bouzoubia, S., Layeb, A., Chikhi, S.: A multi-objective chemical reaction optimisation algorithm for multi-objective travelling salesman problem. Int. J. Innovative Comput. Appl. 6, 87–101 (2014)

    Article  Google Scholar 

  12. Gao, F., Zhou, A., Zhang, G.: An estimation of distribution algorithm based on decomposition for the multi-objective TSP. In: International Conference on Natural Computation (ICNC 2012), pp. 817–821 (2012)

    Google Scholar 

  13. Zhang, Q., Li, H.: MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  14. Ke, L., Zhang, Q., Battiti, R.: MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and ant colony. IEEE Trans. Cybern. 43(6), 1845–1859 (2013)

    Article  Google Scholar 

  15. Iredi, S., Merkle, D., Middendorf, M.: Bi-criterion optimization with multi colony ant algorithms. Evolutionary Multi-criterion Optimization, pp. 359-372. Springer (2001)

    Google Scholar 

  16. He, J.: Solving the multiobjective multiple traveling salesmen problem using membrane algorithm. Bio-inspired Computing, Theories and Applications, Communications in Computer and Information Science, vol. 472, pp. 171–175 (2014)

    Google Scholar 

  17. Xu, J., Lam, A.Y.S., Li, V.O.K.: Chemical reaction optimization for the grid scheduling problem. In: Proceedings of the IEEE ICC, pp. 1–5 (2010)

    Google Scholar 

  18. Sun, J., Wang, Y., Li, J., Gao, K.: Hybrid algorithm based on chemical reaction optimization and lin-kernighan local search for the traveling salesman problem. In: Seventh International Conference on Natural Computation, pp. 1518–1521 (2011)

    Google Scholar 

  19. Truong, T.K., Li, K., Xua, Y.: Chemical reaction optimization with greedy strategy for the 0–1 knapsack problem. Appl. Soft Comput. 13, 1774–1780 (2013)

    Article  Google Scholar 

  20. Li, J.Q., Pan, Q.K.: Chemical reaction optimization for flexible job-shop scheduling problems with maintenance activity. Appl. Soft Comput. 12, 2896–2912 (2012)

    Article  Google Scholar 

  21. Reinelt, G.: TSPLIB a traveling salesman problem library. ORSA J. Comput. 3, 376–384 (1991)

    Article  MATH  Google Scholar 

  22. Zitzler, E.: Evolutionary algorithms for multi-objective optimization: methods and applications. Ph.D. thesis, Swiss Federal Institute of Technology, Zurich (1999)

    Google Scholar 

  23. Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithms for multi-objective optimization. Technical report, Institute of technology, Kanpur India (2000)

    Google Scholar 

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Authors and Affiliations

  1. MISC Laboratory, Department of Fundamental Computer Science and Its Applications, Constantine 2 Abdelhamid Mehri University, 25017, Constantine, Algeria

    Samira Bouzoubia, Abdesslem Layeb & Salim Chikhi

Authors
  1. Samira Bouzoubia
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  2. Abdesslem Layeb
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  3. Salim Chikhi
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Corresponding author

Correspondence to Samira Bouzoubia .

Editor information

Editors and Affiliations

  1. Faculty of New Informationand Commu Tech, University of Constantine2, Constantine, Algeria

    Salim Chikhi

  2. Fac ofTechn, GeCoDe Laboratory, University of Saida, Saida, Algeria

    Abdelmalek Amine

  3. Faculty of New Info& Commun Tech, University of Constantine2, Constantine, Algeria

    Allaoua Chaoui

  4. Faculty of Sci & Tech,Dept of maths & CS, University of El Oued, El-Oued,, Algeria

    Mohamed Khireddine Kholladi

  5. Faculty of New Informationand Com Tech, University of Constantine2, Constantine, Algeria

    Djamel Eddine Saidouni

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Bouzoubia, S., Layeb, A., Chikhi, S. (2016). Enhanced Chemical Reaction Optimization for Multi-objective Traveling Salesman Problem. In: Chikhi, S., Amine, A., Chaoui, A., Kholladi, M., Saidouni, D. (eds) Modelling and Implementation of Complex Systems. Lecture Notes in Networks and Systems, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-33410-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-33410-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33409-7

  • Online ISBN: 978-3-319-33410-3

  • eBook Packages: EngineeringEngineering (R0)

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