Abstract
Contemporary evolutionary multiobjective optimisation techniques are becoming increasingly focussed on the notions of archiving, explicit diversity maintenance and population-based Pareto ranking to achieve good approximations of the Pareto front. While it is certainly true that these techniques have been effective, they come at a significant complexity cost that ultimately limits their application to complex problems. This paper proposes a new model that moves away from explicit population-wide Pareto ranking, abandons both complex archiving and diversity measures and incorporates a continuous accretion-based approach that is divergent from the discretely generational nature of traditional evolutionary algorithms. Results indicate that the new approach, the Combative Accretion Model (CAM), achieves markedly better approximations than NSGA across a range of well-recognised test functions. Moreover, CAM is more efficient than NSGAII with respect to the number of comparisons (by an order of magnitude), while achieving comparable, and generally preferable, fronts.
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References
Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic, New York (2002)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Srinivas, N., Deb, K.: Multiobjective Optimization using Non-dominated Sorting in Genetic Algorithms. Evolutionary Computation 2(3), 221–248 (1994)
Fonesca, C.M., Fleming, P.J.: Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In: Genetic Algorithms: Proceedings of the Fifth International Conference, San Mateo, CA (1993)
Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A Fast Elitist Multi-Objective Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In: Proceedings of the Parallel Problem Solving from Nature Conference VI, pp. 849–858 (2000)
Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. LNCS. Springer, Heidelberg (1999)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization. Evolutionary Methods for Design, Optimization and Control (2002)
Knowles, J., Corne, D.: The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation. In: Proceedings of the Congress on Evolutionary Computation, IEEE Press, Washington (1999)
Corne, D.W., Knowles, J.D., Oates, M.J.: The Pareto-Envelope based Selection Algorithm for Multiobjective Optimisation. PPSN VI, 869–878 (2000)
Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J.: PESA-II: Region-Based Selection in Evolutionary Multiobjective Optimization. In: Proceedings of GECCO (2001)
Horn, J., Nafpliotis, N., Goldberg, D.E.: A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In: Proceedings of the First CEC (1994)
Zitzler, E., Thiele, L., Deb, K.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. In: 1999 Genetic and Evolutionary Computation Conference (1999)
Jensen, M.T.: Reducing the Run-time Complexity of Multi-Objective EAs: The NSGA-II and Other Algorithms. IEEE Transactions on Evolutionary Computation 7(5) (2003)
Socha, K., Kisiel-Dorohinicki, M.: Agent-Based Evolutionary Multiobjective Optimisation. In: Proceedings of CEC 2002 - Congress on Evolutionary Computation (2002)
Laumanns, M., Rudolph, G., Schwefel, H.-P.: A Spatial Predator-Prey Approach to Multi-Objective Optimization: a Preliminary Study. In: PPSN V (1998)
Berry, A., Vamplew, P.: A Simplified Artificial Life Model for Multiobjective Optimisation: A Preliminary Report. In: The Congress on Evolutionary Computation (CEC), Canberra, ACT (2003)
Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combing Convergence and Diversity in Evolutionary Multi-Objective Optimization. Evolutionary Computation 10(3) (2002)
Knowles, J.D., Corne, D.W.: Properties of an Adaptive Archiving Algorithm for Storing Nondominated Vectors. IEEE Transactions on Evolutionary Computation 7(2) (2003)
Horn, J.: Multicriterion Decision Making. In: Handbook of Evolutionary Computation (1997)
Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Optimization with Messy Genetic Algorithms. In: Symposium on Applied Computing, pp. 470–476 (2000)
Zydallis, J.B., Van Veldhuizen, D.A., Lamont, G.: A Statistical Comparison of Multiobjective Evolutionary Algorithms Including the MOMGA-II. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, p. 226. Springer, Heidelberg (2001)
Okabe, T., Jin, Y., Sendhoff, B.: A Critical Survey of Performance Indices for Multi-Objective Optimisation. In: Congress on Evolutionary Computation, Canberra (2003)
Zitzler, E., Thiele, L., Laumanns, M., Fonesca, C.M., Grunert de Fonesca, V.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation (2002) (Accepted for Publication)
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Berry, A., Vamplew, P. (2005). The Combative Accretion Model – Multiobjective Optimisation Without Explicit Pareto Ranking. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_6
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DOI: https://doi.org/10.1007/978-3-540-31880-4_6
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