Abstract
The average ranking (AR) method has been shown highly effective to provide sufficient selection pressure searching towards Pareto optimal set in many-objective optimization. However, as lack of diversity maintenance mechanism, the obtained final set may only concentrate in a subregion of Pareto front. In this paper, we propose a diversity maintenance strategy for AR to balance convergence and diversity during evolution process. We employ grid to define an adaptive neighborhood for each individual, whose size varies with the number of objectives. Moreover, a layering selection scheme integrates it and AR to pick out well-converged individuals and prohibit or postpone the archive of adjacent individuals. From an extensive comparative study with original AR and two other diversity maintenance methods, the proposed method shows a good balance among convergence, uniformity and spread.
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References
Purshouse, R.C., Fleming, P.J.: 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)
Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: A short review. In: 2008 Congress on Evolutionary Computation (CEC 2008), pp. 2424–2431. IEEE Service Center, Hong Kong (2008)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Evolutionary Methods for Design, Optimisation and Control, CIMNE, Barcelona, Spain, pp. 95–100 (2002)
Corne, D., Knowles, J.: Techniques for highly multiobjective optimization: Some non-dominated points are better than others. In: Proc. of 2007 Genetic and Evolutionary Computation Conference (GECCO 2007), London, pp. 773–780 (July 2007)
Jaimes, A.L., Quintero, L.V.S., Coello Coello, A.C.: Ranking Methods in Many-objective Evolutionary Algorithms. In: Chiong, R. (ed.) Nature-Inspired Algorithms for Optimisation, pp. 413–434. Springer, Berlin (2009)
Adra, S.F., Fleming, P.J.: A Diversity Management Operator for Evolutionary Many-Objective Optimisation. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 81–94. Springer, Heidelberg (2009)
Bentley, P.J., Wakefield, J.P.: Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms. In: Soft Computing in Engineering Design and Manufacturing, Part 5, London, pp. 231–240 (June 1997)
Knowles, J., Corne, D.: Properties of an Adaptive Archiving Algorithm for Storing Nondominated Vectors. IEEE Trans. Evol. Comput. 7(2), 100–116 (2003)
Wagner, T., Beume, N., Naujoks, B.: Pareto-, aggregation-, and indicator-based methods in many-objective optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 742–756. Springer, Heidelberg (2007)
Li, M., Zheng, J.: Spread Assessment for Evolutionary Multi-Objective Optimization. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 216–230. Springer, Heidelberg (2009)
Deb, K., Jain, S.: Running Performance Metrics for Evolutionary Multi-Objective Optimization. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL 2002), pp. 13–20 (2002)
Goh, C.K., Tan, K.C.: An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization. IEEE Trans. Evol. Comput. 11(3), 354–381 (2007)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. in Evolutionary Multiobjective Optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Theoretical Advances and Applications, pp. 105–145. Springer, Berlin (2005)
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Li, M., Zheng, J., Li, K., Yuan, Q., Shen, R. (2010). Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_65
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DOI: https://doi.org/10.1007/978-3-642-15844-5_65
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