Multiobjective Distinct Candidates Optimization (MODCO): A Cluster-Forming Differential Evolution Algorithm
Pages 525 - 539
Abstract
Traditionally, Multiobjective Evolutionary Algorithms (MOEAs) aim at approximating the entire true pareto-front of their input problems. However, the actual number of solutions with different trade-offs between objectives in a resulting pareto-front is often too large to be applicable in practice. The new field Multiobjective Distinct Candidates Optimization (MODCO) research is concerned with the optimization of a low and user-defined number of clearly distinct candidates. This dramatically decreases the amount of post-processing needed in the decision making process of which solution to actually implement, as described in our related technical repport "Multiobjective Distinct Candidates Optimization (MODCO): A new Branch of Multiobjective Optimization Research" [9].
In this paper, we introduce the first algorithm designed for the challenges of MODCO; providing a given number of distinct solutions as close as possible to the true pareto-front. The algorithm is using subpopulations to enforce clusters of solutions, in such a way that the number of clusters formed can be set directly. The algorithm is based on the Differential Evolution for Multiobjective Optimization (DEMO) algorithm versions, but is exchanging the crowding/density measure with two alternating secondary fitness measures. Applying these measures ensures that subpopulations are attracted towards knee regions while also making them repel each other if they get too close to one another. This way subpopulations traverse different parts of the objective space while forming clusters each returning a single distinct solution.
References
[1]
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on Evolutionary Computation 6, 182-197 (2002)
[2]
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2002)
[3]
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland (2001)
[4]
Price, K.V., Storn, R.: Differential Evolution - a simple evolution strategy for fast optimization. Dr. Dobb's journal 22, 18-24 (1997)
[5]
Robič, T., Filipič, B.: DEMO: Differential Evolution for Multiobjective Optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 520-533. Springer, Heidelberg (2005)
[6]
Robič, T., Filipič, B.: Differential Evolution versus Genetic Algorithms in Multiobjective Optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 257-271. Springer, Heidelberg (2007)
[7]
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Test Problems for Evolutionary Multi-Objective Optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, pp. 825-830 (2002)
[8]
Branke, J., Deb, K., Dierolf, H., Osswald, M.: Finding Knees in Multi-objective Optimization. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 722-731. Springer, Heidelberg (2004)
[9]
Ursem, R.K., Justesen, P.D.: Multiobjective Distinct Candidates Optimization (MODCO) - A new Branch of Multiobjective Optimization Research. Technical Report no. 2008-01, Dept. of Computer Science, University of Aarhus (2008), Download: http://www.daimi.au.dk/~ursem/publications/Ursem_EMO2009_MODCO.pdf
[10]
Kukkonen, S., Lampinen, J.: GDE3: The third Evolution Step of Generalized differential evolution. In: Proceedings of the 2005 Congress on Evolutionary Computation, CEC 2005, pp. 443-450 (2005)
[11]
Karthis, S., Deb, K., Miettinen, K.: A Local Search Based Evolutionary Multiobjective Optimization for Fast and Accurate Convergence. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 815-824. Springer, Heidelberg (2008)
- Multiobjective Distinct Candidates Optimization (MODCO): A Cluster-Forming Differential Evolution Algorithm
Recommendations
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
Partly due to lack of test problems, the impact of the Pareto set (PS) shapes on the performance of evolutionary algorithms has not yet attracted much attention. This paper introduces a general class of continuous multiobjective optimization test ...
Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method
Most current approaches in the evolutionary multiobjective optimization literature concentrate on adapting an evolutionary algorithm to generate an approximation of the Pareto frontier. However, finding this set does not solve the problem. The decision-...
Multi-objective Distinct Candidates Optimization: Locating a few highly different solutions in a circuit component sizing problem
Abstract: Traditional multi-objective optimization algorithms typically return several hundred non-dominated solutions. From a practical point of view, a small set of 5-10 distinct candidates is often preferred because post-processing many solutions may ...
Comments
Information & Contributors
Information
Published In
April 2009
583 pages
ISBN:9783642010194
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 21 April 2009
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 28 Dec 2024