Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
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)
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)
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)
Hart, W.E.: Adaptive Global Optimization with Local Search. Ph. D. Thesis, University of California, San Diego (1994)
Hart, W.E., Krasnogor, N., Smith, J.E. (eds.): Recent Advances in Memetic Algorithms, pp. 3–27. Springer, Berlin (2005)
Hughes, E.J.: Evolutionary Many-Objective Optimization: Many Once or One Many? In: Proc. of IEEE Congress on Evolutionary Computation, pp. 222–227 (2005)
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)
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)
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)
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)
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)
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)
Jaszkiewicz, A.: Genetic Local Search for Multi-Objective Combinatorial Optimization. European Journal of Operational Research 137, 50–71 (2002)
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)
Jaszkiewicz, A.: On the Computational Efficiency of Multiple Objective Metaheuristics: The Knapsack Problem Case Study. European Journal of Operational Research 158, 418–433 (2004)
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)
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)
Krasnogor, N.: Studies on the Theory and Design Space of Memetic Algorithms. Ph. D. Thesis, University of the West of England, Bristol (2002)
Lacomme, P., Prins, C., Sevaux, M.: A Genetic Algorithm for a Bi-Objective Capacitated Arc Routing Problem. Computers & Operations Research 33, 3473–3493 (2006)
Land, M.W.S.: Evolutionary Algorithms with Local Search for Combinatorial Optimization. Ph. D. Thesis, University of California, San Diego (1998)
Merz, P.: Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscape and Effective Search Strategy. Ph. D. Thesis, University of Siegen, Siegen (2000)
Merz, P., Freisleben, B.: Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem. IEEE Trans. on Evolutionary Computation 4, 337–352 (2000)
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)
Ong, Y.S., Keane, A.J.: Meta-Lamarckian Learning in Memetic Algorithms. IEEE Trans. on Evolutionary Computation 8, 99–110 (2004)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ishibuchi, H., Hitotsuyanagi, Y., Tsukamoto, N., Nojima, Y. (2008). Use of Heuristic Local Search for Single-Objective Optimization in Multiobjective Memetic Algorithms. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_74
Download citation
DOI: https://doi.org/10.1007/978-3-540-87700-4_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87699-1
Online ISBN: 978-3-540-87700-4
eBook Packages: Computer ScienceComputer Science (R0)