Abstract
This paper proposes a new multiobjective evolutionary approach—the dynamic archive evolution strategy (DAES) to investigate the adaptive balance between proximity and diversity. In DAES, a novel dynamic external archive is proposed to store elitist individuals as well as relatively better individuals through archive increase scheme and archive decrease scheme. Additionally, a combinatorial operator that inherits merits from Gaussian mutation of proximity exploration and Cauchy mutation of diversity preservation is elaborately devised. Meanwhile, a complete nondominance selection ensures maximal pressure of proximity exploitation while a corresponding fitness assignment ensures the similar pressure of diversity preservation. By graphical presentation and performance metrics on three prominent benchmark functions, DAES is found to outperform three state-of-the-art multiobjective evolutionary algorithms to some extent in terms of finding a near-optimal, well-extended and uniformly diversified Pareto optimal front.
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
Fonseca, C.M., Fleming, P.J.: An Overview of Evolutionary Algorithms in Multiobjective Optimization. Evolutionary Computation 1, 1–16 (1995)
Srinivas, N., Deb, K.: Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation 2, 221–248 (1994)
Deb, K., Agrawal, S., Pratap, A., et al.: A Fast Elitist Nondominated Sorting Genetic Algorithm for Multiobjective Optimization: NSGA-II. Evolutionary Computation 2, 182–197 (2002)
Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. Evolutionary Computation 1, 257–271 (1999)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In: EUROGEN 2001, Athens, Greece (2001)
Knowles, J.D., Corne, D.W.: Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation 2, 149–172 (2000)
Knowles, J.D., Corne, D.W.: M-PAES: A Memetic Algorithm for Multiobjective Optimization. In: Proceedings of the 2000 congress on evolutionary computation, pp. 325–332. IEEE Press, Piscataway (2000)
Mahfoud, S.W.: Genetic Drift in Sharing Methods. In: Grefenstette, J.J. (ed.) Proceedings of the 1st IEEE conference on evolutionary computation, pp. 67–72. IEEE Press, Piscataway (1994)
Schwefel, H.P., Back, T.: A Survey of Evolutionary Strategies. In: Belew, R. (ed.) Proceedings of the 4th international conference on genetic algorithms, pp. 92–99. Morgan Kaufmann publishers, San Mateo (1991)
Bosman, P.A.N., Thierens, D.: The Balance Between Proximity and Diversity in Multi- objective Evolutionary Algorithms. Evolutionary Computation 7, 174–188 (2003)
Yao, X., Liu, Y.: Fast Evolutionary Programming. In: Proceedings of the 5th annual conference on evolutionary programming, pp. 451–460. MIT Press, Cambridge (1996)
Gomes, J.R., Saavedra, O.R.: A Cauchy-based Evolution Strategy for Solving the Reactive Power Dispatch Problem. Electrical Power and Energy Systems 24, 277–283 (2002)
Shi, L.B., Xu, G.Y.: Self-adaptive Evolutionary Programming and its Application to Multiobjective Optimal Operation of Power Systems. Electric Power Systems Research 57, 181–187 (2001)
Bosman, P.A.N., Thierens, D.: Multiobjective Optimization with Diversity Preserving Mixture-based Iterated Density Estimation Evolutionary Algorithms. Int. J. Approx. Reasoning. 31, 259–289 (2002)
Tan, K.C., Lee, T., Khor, E.: Evolutionary Algorithms with Dynamic Population Size and Local Exploration for Multiobjective Optimization. Evolutionary Computation 12, 565–588 (2001)
Sarker, R., Liang, K.H., Newtom, C.: A New Multiobjective Evolutionary Algorithm. European Journal of Operational Research 140, 12–23 (2002)
Schwefel, H.P.: Numerical Optimization for Computer Models, pp. 129–132. John Wiley, Chichester (1981)
Hu, X., Eberhart, R.C.: Multiobjective Optimization Using Dynamic Neighborhood Particle Swarm Optimization. In: Proceedings of the 2002 congress on evolutionary computation, pp. 1677–1681. IEEE Press, Piscataway (2002)
Back, T., Schwefel, H.P.: An Overview of Evolutionary Algorithm for Parameter Optimization. Evolutionary Computation 2, 1–23 (1993)
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 2, 173–195 (2000)
van Veldhuizen, D.A., Lamont, G.B.: On Measuring Multiobjective Evolutionary Algorithm Performance. In: Proceedings of the 2000 congress on evolutionary computation, pp. 204–211. IEEE Press, Piscataway (2000)
Chambers, J.M., Cleveland, W.S., Kleiner, B., et al.: Graphical Methods for Data Analysis. Wadsworth & Brooks/Cole, Pacific Grove (1983)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Min, Y.S., Guo, S.D., Jie, L.Y. (2005). Dynamic Archive Evolution Strategy for Multiobjective Optimization. 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_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-31880-4_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24983-2
Online ISBN: 978-3-540-31880-4
eBook Packages: Computer ScienceComputer Science (R0)