Abstract
We introduce a new multiobjective evolutionary algorithm called PESA (the Pareto Envelope-based Selection Algorithm), in which selection and diversity maintenance are controlled via a simple hyper-grid based scheme. PESA’s selection method is relatively unusual in comparison with current well known multiobjective evolutionary algorithms, which tend to use counts based on the degree to which solutions dominate others in the population. The diversity maintenance method is similar to that used by certain other methods. The main attraction of PESA is the integration of selection and diversity maintenance, whereby essentially the same technique is used for both tasks. The resulting algorithm is simple to describe, with full pseudocode provided here and real code available from the authors. We compare PESA with two recent strong-performing MOEAs on some multiobjective test problems recently proposed by Deb. We find that PESA emerges as the best method overall on these problems.
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References
Czyzak, P. and Jaszkiewicz, A. (1998). Pareto simulated annealing-a meta-heuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis 7, 34–47.
Deb, K. (1998). Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Technical Report CI-49/98, Department of Computer Science, University of Dortmund.
Fonseca, C. M. and Fleming, P. J. (1995). An Overview of Evolutionary Algorithms in Multiobjective Optimization. Evolutionary Computation 3, 1–16.
Fonseca, C. M. and Fleming, P. J. (1995a). On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers. In Voigt, H-M., Ebeling, W., Rechenberg, I. and Schwefel, H-P., editors, Parallel Problem Solving From Nature-PPSN IV, pages 584–593, Springer.
Gandibleux, X., Mezdaoui N. and Freville, A. (1996). A tabu search procedure to solve multiobjective combinatorial optimization problems. In Caballero, R. and Steuer, R., editors, in Proceedings volume of Multiple Objective Programming and Goal Programming’ 96, Springer-Verlag.
Hansen, M. P. (1996). Tabu Search for Multiobjective Optimization: MOTS. Presented at MCDM’ 91, Cape Town, South Africa, Jan 6–10.
Hansen, M. P. (1997). Generating a Diversity of Good Solutions to a Practical Combinatorial Problem using Vectorized Simulated Annealing. Submitted to Control and Cybernetics, August 1997.
Horn, J., Nafpliotis, N., Goldberg, D.E. (1994). A niched Pareto genetic algorithm for multiobjective optimization, in Proceedings of the First IEEE Conference on Evolutionary Computation IEEE World Congress on Computational Intelligence, Volume 1, pages 67–72. Piscataway, NJ: IEEE Service Centre.
Horn, J. and Nafpliotis, N. (1994). Multiobjective Optimization Using The Niched Pareto Genetic Algorithm. IlliGAL Report 93005, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, Champaign.
Knowles, J. D. and Corne, D. W. (1999). Local Search, Multiobjective Optimisation and the Pareto Archived Evolution Strategy, in Proceedings of the Third Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems, pp. 209–216.
Knowles, J. D. and Corne, D. W. (2000). Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2):149–172.
Parks, G. T., Miller, I. (1998). Selective Breeding in a Multiobjective Genetic Algorithm. In Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V), pages 250–259. Springer.
Srinivas, N., Deb, K. (1994). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221–248.
Zitzler, E. and Thiele, L. (1999). Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 2(4), 257–272.
Zitzler, E., Deb, K. and Thiele, L. (1999) Comparison of multiobjective evolutionary algorithms: empirical results. Technical report 70, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich.
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Corne, D.W., Knowles, J.D., Oates, M.J. (2000). The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_82
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DOI: https://doi.org/10.1007/3-540-45356-3_82
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