Summary
An exploratory multiobjective evolutionary algorithm (EMOEA) that integrates the features of tabu search and evolutionary algorithm for multiobjective (MO) optimization is presented in this chapter. The method incorporates the tabu restriction in individual examination and preservation in order to maintain the search diversity in evolutionary MO optimization, which subsequently helps to prevent the search from trapping in local optima as well as to promote the evolution towards the global trade-offs concurrently. The features of the algorithm are examined based upon three benchmark problems. Experimental results show that EMOEA performs well in searching and distributing nondominated solutions along the trade-offs uniformly, and offers a competitive behavior to escape from local optima in a noisy environment.
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
Areibi S, Vannelli A (1993). Circuit partitioning using a Tabu search approach. IEEE International Symposium on Circuits and Systems, vol. 3, pp. 1643–1646
Beyer D A, Ogier R G (1991) . Tabu learning: a neural network search method for solving nonconvex optimization problems. IEEE International Joint Conference on Neural Networks, Vol. 2, pp. 953–961
Braglia M, Melloni R (1995). Tabu search for the single machine sequencing problem with ready times. INRIA/IEEE Symposium on Emerging Technologies and Factory Automation, vol. 2, pp. 395–403
Coello Coello C A (1996). An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design. Ph.D. Thesis, Department of Computer Science, Tulane University, New Orleans, LA
Coello Coello C A, Van Veldhuizen D A, Lamont, G B (2002). Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers
Cvetkovic D, Parmee I C (2002). Preferences and their application in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 42–57
De Falco I, Del Balio R, Tarantino E, Vaccaro R (1994). Improving search by incorporating evolution principles in parallel Tabu Search. IEEE Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 823–828
Deb K (1999). Multi-objective genetic algorithms: Problem difficulties and construction of test problem. Journal of Evolutionary Computation, The MIT Press, vol. 7, no. 3, pp 205–230
Deb K (2001). Multi-Objective Optimization using Evolutionary Algorithms. Chichester, John Wiley & Sons, Ltd
Deb K, Pratap A, Agarwal S, Meyarivan T (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197
Fonseca C M, Fleming P J (1995) . Multi-objective genetic algorithm made easy: Selection, sharing and mating restriction. International Conference on Genetic Algorithm in Engineering Systems: Innovations and Application, UK, pp. 12–14
Fonseca C M, Fleming P J (1998). Multiobjective optimization and multiple constraint handling with evolutionary algorithms-Part I: A unified Formulation. IEEE Transactions on System, Man, and Cybernetics-Part A: System and Humans, vol. 28, no. 1. pp. 26–37
Garrison W G, Hu X S, D’Ambrosio J G (1997). Fitness functions for multiobjective optimization problems: Combining preferences with Pareto rankings. In Belew, R. K., and Vose, M. D., eds., Foundations of Genetic Algorithms 4, Morgan Kaufmann, San Mateo, California, pp. 437–455
Grimm, L G (1993). Statistical Application for Behavioral Sciences. J. Wiley, New York.
Horn J, Nafpliotis N, Goldberg D E (1994). A niched Pareto genetic algorithm for multiobjective optimization. Proceeding of First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, vol. 1, pp. 82–87
Khor E F, Tan K C, Lee T H (2001). Tabu-based exploratory evolutionary algorithm for effective multi-objective optimization, Springer-Verlag Lecture Notes in Computer Science, no. 1993, The First International Conference on Evolutionary Multi-Criteria Optimization (EMO’01), Zurich, Switzerland, pp. 344–358
Kim H, Hayashi Y, Nara K. (1997). An algorithm for thermal unit maintenance scheduling through combined use of GA, SA and TS. IEEE Transactions on Power Systems, vol. 12, no. 1, pp. 329–335
Knowles J D, Corne D W (2000) . Approximating the nondominated front using the Pareto archived evolution strategy. Evolutionary Computation, MIT Press Journals, vol. 8, no. 2, pp. 149–172
Laumanns M, Rudolph G, Schwefel H P (1998). A Spatial Predator-Prey approach to multi-objective optimization: A preliminary study. In Eiben A E, Schoenauer M, Schwefel H P, eds., Parallel Problem Solving From Nature — PPSN V, Springer-Verlag, Amsterdam, Holland, pp. 241–249
Mantawy A H, Abdel-Magid, Y L, Selim S Z (1999). Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem. IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 829–836
Richardson J T, Palmer M R, Liepins G, Hilliard M (1989). Some guidelines for genetic algorithms with penalty functions. In: Schaffer J D, ed., Proceedings of Third Int. Conf. on Genetic Algorithms, pp. 191–197
Schaffer J D, Caruana R A, Eshelman L J, Das R. (1989) . A study of control parameters affecting online performance of genetic algorithms for function optimization. Proceedings of Third International Conference on Genetic Algorithms, pp. 51–60
Srinivas N, Deb K. (1994) . Multiobjective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation, MIT Press Journals, vol. 2, no. 3, pp. 221–248
Tan K C, Lee T H, Khor E F (2002). Evolutionary algorithms for multiobjective optimization: performance assessments and comparisons. Artificial Intelligence Review, vol. 17, no. 4, pp. 251–290
Tan K C, Khor E F, Lee T H, Yang Y J (2003). A tabu-based exploratory evolutionary algorithm for multiobjective optimization’, Artificial Intelligence Review, vol. 19, issue 3, pp. 231–260
The Math Works, Inc. (1998). Using MATLAB, The Math Works Inc., Version 5
Veldhuizen D A V, Lamont G B (1998). Evolutionary computation and convergence to a Pareto front. In: Koza J R, ed., Late Breaking Paper at the Genetic Programming 1998 Conference, Stanford University Bookstore, Stanford University, California, pp. 221–228
Veldhuizen D A V, Lamont G B (1999). Multiobjective evolutionary algorithm test suites. Symposium on Applied Computing, San Antonio, Texas, pp. 351–357
Yagiura M, Ibaraki T. (1996). Metaheuristics as robust and simple optimization tools. IEEE Proceedings of the Congress on Evolutionary Computation, pp. 541–546
Zitzler E, Thiele L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp.257–271
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Khor, E.F., Tan, K.C., Yang, Y.J. (2005). An Evolutionary Algorithm with Tabu Restriction and Heuristic Reasoning for Multiobjective Optimization. In: Jin, Y. (eds) Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44511-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-44511-1_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-06174-5
Online ISBN: 978-3-540-44511-1
eBook Packages: EngineeringEngineering (R0)