Abstract
Multi-objective random one-bit climbers (moRBCs) are one class of stochastic local search-based algorithms that maintain a reference population of solutions to guide their search. They have been shown to perform well in solving multi-objective optimization problems. In this work, we analyze the performance of moRBCs when modified by introducing tabu moves. We also study their behavior when the selection to update the reference population and archive is replaced with a procedure that provides an alternative mechanism for preserving the diversity among the solutions. We use several MNK-landscape models as test instances and apply statistical testings to analyze the results. Our study shows that the two modifications complement each other in significantly improving moRBCs’ performance especially in many-objective problems. Moreover, they can play specific roles in enhancing the convergence and spread of moRBCs.
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References
Purshouse, R.C., Fleming, P.: Conflict, Harmony, and Independence: Relationships in Evolutionary Multi-criterion Optimisation. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 16–30. Springer, Heidelberg (2003)
Khare, V., Yao, X., Deb, K.: Performance Scaling of Multi-objective Evolutionary Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 376–390. Springer, Heidelberg (2003)
Hughes, E.: Evolutionary many-objective optimization: Many once or one many? In: Proceedings of 2005 IEEE Congress on Evolutionary Computation, Edinburgh, pp. 222–227 (2005)
Aguirre, H.E., Tanaka, K.: Working principles, behavior, and performance of MOEAs on MNK-landscapes. European Journal of Operational Research 181(3), 1670–1690 (2007)
Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: A short review. In: Proc. of 2008 IEEE Congress on Evolutionary Computation, Hong Kong, pp. 2424–2431 (2008)
Sato, H., Aguirre, H., Tanaka, K.: Controlling dominance area of solutions and its impact on the performance of mOEAs. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 5–20. Springer, Heidelberg (2007)
Brockhoff, D., Zitzler, E.: Are All Objectives Necessary? On Dimensionality Reduction in Evolutionary Multiobjective Optimization. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 533–542. Springer, Heidelberg (2006)
Saxena, D., Deb, K.: Non-linear Dimensionality Reduction Procedures for Certain Large-Dimensional Multi-objective Optimization Problems: Employing Correntropy and a Novel Maximum Variance Unfolding. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 772–787. Springer, Heidelberg (2007)
Aguirre, H., Tanaka, K.: Many-Objective Optimization by Space Partitioning and Adaptive ε-Ranking on MNK-Landscapes. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 407–422. Springer, Heidelberg (2009)
Jaimes, A., Aguirre, H., Tanaka, K., Coello Coello, C.: Objective Space Partitioning Using Conflict Information for Many-Objective Optimization. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 657–666. Springer, Heidelberg (2010)
Deb, K., Sundar, J.: Reference point based multi-objective optimization using evolutionary algorithms. In: Proc. of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 635–642. ACM, New York (2006)
Hughes, E.: MSOPS-II: A general purpose many-objective optimiser. In: Proc. of 2007 IEEE Congress on Evolutionary Computation, Singapore, pp. 3944–3951 (2007)
Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research 181(3), 1653–1669 (2007)
Thiele, L., Miettinen, K., Korhonen, P., Molina, J.: A preference-based evolutionary algorithm for multi-objective optimization. Evolutionary Computation 17(3), 411–436 (2009)
Paquete, L.: Stochastic local search algorithms for multiobjective combinatorial optimization: methods and analysis. PhD thesis, FB Informatik, TU Darmstadt, Germany (2005)
Ishibuchi, H., Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics 28(3), 392–403
Stützle, T.: Iterated local search for the quadratic assignment problem. European Journal of Operational Research 174, 1519–1539 (2006)
Pasia, J.M., Doerner, K.F., Hartl, R.F., Reimann, M.: A Population-Based Local Search for Solving a Bi-objective Vehicle Routing Problem. In: Cotta, C., van Hemert, J. (eds.) EvoCOP 2007. LNCS, vol. 4446, pp. 166–175. Springer, Heidelberg (2007)
Paquete, L., Stuetzle, T.: A study of stochastic local search algorithms for the biobjective qap with correlated flow matrices. European Journal of Operational Research 169(3), 943–959 (2006)
Aguirre, H.E., Tanaka, K.: Random bit climbers on multiobjective MNK-Landscapes: Effects of memory and population climbing. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A(1), 334–345
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation:NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evolutionary Computation 3(4), 257–271 (1999)
Kauffman, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, Oxford (1993)
Fonseca, C., Paquete, L., López-Ibáñez, M.: An improved dimension-sweep algorithm for the hypervolume indicator. In: IEEE Congress on Evolutionary Computation, Vancouver, Canada, pp. 1157–1163 (2006)
Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - A comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)
Hollander, M., Wolfe, D.A.: Nonparametric Statistical Methods. John Wiley & Sons, New York (1999)
Knowles, J., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. Technical Report TIK-Report No. 214, Computer Engineering and Networks Laboratory, ETH Zurich, Gloriastrasse 35, ETH-Zentum, 8092 Zurich, Switzerland (February 2006)
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Pasia, J.M., Aguirre, H., Tanaka, K. (2011). Improved Random One-Bit Climbers with Adaptive ε-Ranking and Tabu Moves for Many-Objective Optimization. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds) Evolutionary Multi-Criterion Optimization. EMO 2011. Lecture Notes in Computer Science, vol 6576. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19893-9_13
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DOI: https://doi.org/10.1007/978-3-642-19893-9_13
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