Use of Heuristic Local Search for Single-Objective Optimization in Multiobjective Memetic Algorithms

  • Hisao Ishibuchi
  • Yasuhiro Hitotsuyanagi
  • Noritaka Tsukamoto
  • Yusuke Nojima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


This paper proposes an idea of using heuristic local search procedures specific for single-objective optimization in multiobjective genetic local search (MOGLS). A large number of local search techniques have been studied for various combinatorial optimization problems. Thus we may have a situation where a powerful local search procedure specific for a particular objective is available in multiobjective optimization. Such a local search procedure, however, can improve only a single objective. Moreover, it may have severe side-effects on the other objectives. For example, in a scheduling problem, an insertion move of a job with the maximum delay to an earlier position in a current schedule is likely to improve only the maximum tardiness. In this paper, we assume a situation where each objective has its own heuristic local search procedure. First we explain our MOGLS algorithm, which is the hybridization of NSGA-II and weighted sum-based local search. Next we propose an idea of using heuristic local search procedures specific for single-objective optimization in MOGLS. Then we implement the proposed idea as a number of variants of MOGLS. These variants are different from each other in the choice of a heuristic local search procedure. We examine three schemes: random, probabilistic and deterministic. Finally we examine the performance of each variant through computational experiments on multiobjective 0/1 knapsack problems with two, three and four objectives. It is shown that the use of heuristic local search procedures and their appropriate choice improve the performance of MOGLS.


Local Search Weight Vector Pareto Front Multiobjective Optimization Knapsack Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bersini, H., Dorigo, M., Langerman, S., Seront, G., Gambardella, L.: Results of the First International Contest on Evolutionary Optimization. In: Proc. of 1996 IEEE International Conference on Evolutionary Computation, pp. 611–615 (1996)Google Scholar
  2. 2.
    Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)zbMATHGoogle Scholar
  3. 3.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6, 182–197 (2002)CrossRefGoogle Scholar
  4. 4.
    Fonseca, C.M., Fleming, P.J.: On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 584–593. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  5. 5.
    Freisleben, B., Merz, P.: A Genetic Local Search Algorithm for Solving Symmetric and Asymetric Traveling Salesman Problems. In: Proc. of 1996 IEEE International Conference on Evolutionary Computation, pp. 616–621 (1996)Google Scholar
  6. 6.
    Hart, W.E.: Adaptive Global Optimization with Local Search. Ph. D. Thesis, University of California, San Diego (1994)Google Scholar
  7. 7.
    Hart, W.E., Krasnogor, N., Smith, J.E. (eds.): Recent Advances in Memetic Algorithms, pp. 3–27. Springer, Berlin (2005)zbMATHGoogle Scholar
  8. 8.
    Hughes, E.J.: Evolutionary Many-Objective Optimization: Many Once or One Many? In: Proc. of IEEE Congress on Evolutionary Computation, pp. 222–227 (2005)Google Scholar
  9. 9.
    Ishibuchi, H., Murata, T.: A Multi-Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling. IEEE Trans. on Systems, Man, and Cybernetics - Part C: Applications and Reviews 28, 392–403 (1998)CrossRefGoogle Scholar
  10. 10.
    Ishibuchi, H., Murata, T., Tomioka, S.: Effectiveness of Genetic Local Search Algorithms. In: Proc. of 7th International Conference on Genetic Algorithms, pp. 505–512 (1997)Google Scholar
  11. 11.
    Ishibuchi, H., Narukawa, K.: Some Issues on the Implementation of Local Search in Evolutionary Multiobjective Optimization. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 1246–1258. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Ishibuchi, H., Tsukamoto, N., Hitotsuyanagi, Y., Nojima, Y.: Effectiveness of Scalability Improvement Attempts on the Performance of NSGA-II for Many-Objective Problems. In: Proc. of Genetic and Evolutionary Computation Conference (in press, 2008)Google Scholar
  13. 13.
    Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary Many-Objective Optimization: A Short Review. In: Proc. of 2008 Congress on Evolutionary Computation, pp. 2424–2431 (2008)Google Scholar
  14. 14.
    Ishibuchi, H., Yoshida, T., Murata, T.: Balance between Genetic Search and Local Search in Memetic Algorithms for Multiobjective Permutation Flowshop Scheduling. IEEE Trans. on Evolutionary Computation 7, 204–223 (2003)CrossRefGoogle Scholar
  15. 15.
    Jaszkiewicz, A.: Genetic Local Search for Multi-Objective Combinatorial Optimization. European Journal of Operational Research 137, 50–71 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Jaszkiewicz, A.: On the Performance of Multiple-Objective Genetic Local Search on the 0/1 Knapsack Problem - A Comparative Experiment. IEEE Trans. on Evolutionary Computation 6, 402–412 (2002)CrossRefGoogle Scholar
  17. 17.
    Jaszkiewicz, A.: On the Computational Efficiency of Multiple Objective Metaheuristics: The Knapsack Problem Case Study. European Journal of Operational Research 158, 418–433 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Knowles, J.D., Corne, D.W.: M-PAES: A Memetic Algorithm for Multiobjective Optimization. In: Proc. of 2000 IEEE Congress on Evolutionary Computation, pp. 325–332 (2000)Google Scholar
  19. 19.
    Knowles, J.D., Corne, D.W.: A Comparison of Diverse Approaches to Memetic Multiobjective Combinatorial Optimization. In: Proc. of 2000 Genetic and Evolutionary Computation Conference Workshop Program: WOMA I, pp. 103–108 (2000)Google Scholar
  20. 20.
    Krasnogor, N.: Studies on the Theory and Design Space of Memetic Algorithms. Ph. D. Thesis, University of the West of England, Bristol (2002)Google Scholar
  21. 21.
    Lacomme, P., Prins, C., Sevaux, M.: A Genetic Algorithm for a Bi-Objective Capacitated Arc Routing Problem. Computers & Operations Research 33, 3473–3493 (2006)CrossRefzbMATHGoogle Scholar
  22. 22.
    Land, M.W.S.: Evolutionary Algorithms with Local Search for Combinatorial Optimization. Ph. D. Thesis, University of California, San Diego (1998)Google Scholar
  23. 23.
    Merz, P.: Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscape and Effective Search Strategy. Ph. D. Thesis, University of Siegen, Siegen (2000)Google Scholar
  24. 24.
    Merz, P., Freisleben, B.: Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem. IEEE Trans. on Evolutionary Computation 4, 337–352 (2000)CrossRefGoogle Scholar
  25. 25.
    Moscato, P.: Memetic Algorithms: A Short Introduction. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 219–234. McGraw-Hill, London (1999)Google Scholar
  26. 26.
    Ong, Y.S., Keane, A.J.: Meta-Lamarckian Learning in Memetic Algorithms. IEEE Trans. on Evolutionary Computation 8, 99–110 (2004)CrossRefGoogle Scholar
  27. 27.
    Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of Adaptive Memetic Algorithms: A Comparative Study. IEEE Trans. on Systems, Man, and Cybernetics: Part B - Cybernetics 36, 141–152 (2006)Google Scholar
  28. 28.
    Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Trans. on Evolutionary Computation 3, 257–271 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hisao Ishibuchi
    • 1
  • Yasuhiro Hitotsuyanagi
    • 1
  • Noritaka Tsukamoto
    • 1
  • Yusuke Nojima
    • 1
  1. 1.Department of Computer Science and Intelligent Systems, Graduate School of EngineeringOsaka Prefecture UniversityOsakaJapan

Personalised recommendations