Abstract
The characteristics of the search space (its size and shape as well as solution density) are key issues in the application of evolutionary algorithms to real-world problems. Often some regions are crowded and other regions are almost empty. Therefore, some techniques must be used to avoid solutions too close (which the decision maker is indifferent to) and to allow all the regions of interest to be adequately represented in the population. In this paper the concept of δ-non-dominance is introduced which is based on indifference thresholds. Experiments dealing with the use of this technique in the framework of an evolutionary approach are reported to provide decision support in the identification and selection of electric load control strategies.
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Chen, J., Lee, F.N., Breipohl, A.M., Adapa, R.: Scheduling direct load control to minimize system operation cost. IEEE Trans. on Power Systems 10(4), 1994–2001 (1995)
Coello, C.A.C., Van Veldhuizen, D., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Dordrecht (2002)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II, KanGAL Report No. 200001 (2000)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Ltd., New York (2001)
De Jong, K.A.: An analysis of the behaviour of a classe of genetic adaptive systems. Doctoral Thesis, University of Michigan (1975)
Fonseca, C.M., Fleming, P.J.: On the performance assessment and comparison of stochastic multiobjective optimizers. In. Voigt, Ebeling, Rechenberg, Schwefel (eds), Proceedings of Parallel Problem Solving from Nature IV, pp. 584–593 (1996)
Fonseca, C.M., Fleming, P.J.: Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms-Part I: A Unified Formulation. IEEE Trans. on Systems, Man, and Cybernetics-Part A: Systems and Humans 28(1), 26–37 (1998)
Goldberg, D.E.: Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, Reading (1989)
Gomes, A., Martins, A.G., Figueiredo, R.: Simulation-based Assessment of Electric Load Management Programs. International Journal of Energy Research 23, 169–181 (1999)
Gomes, A., Antunes, C.H., Martins, A.G.: A multiple objective evolutionary approach for the design and selection of load control strategies. IEEE Trans. on Power Systems (forthcoming)
Knowles, J.D., Corne, D.W.: Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation Journal 89(2), 149–172 (2000)
Laummans, M., Thiele, L., Deb, K., Zitzler, E.: Combining Convergence and Diversity in Evolutionary Multi-Objective Optimization. Evolutionary Computation 10(3) (2002)
Ng, K.-H., Sheble, G.B.: Direct load control - A profit-based load management using linear programming. IEEE Trans. on Power Systems 13(2), 688–695 (1998)
Osyczka, A.: Evolutionary Algorithms for Single and Multicriteria Design Optimization. Physica-Verlag, Heidelberg (2002)
Zitzler, E., Thiele, L.: An Evolutionary Algorithm for Multiobjective Optimization: The Strength Pareto Approach, TIK Report 43; Computer Engineering and Communication Networks Lab; Swiss Federal Institute of Technology; Zurich; Switzerland (May 1998)
Zitzler, E., Thiele, L., Deb, K.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)
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Gomes, A., Antunes, C.H., Martins, A.G. (2004). Dealing with Solution Diversity in an EA for Multiple Objective Decision Support – A Case Study. In: Gottlieb, J., Raidl, G.R. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2004. Lecture Notes in Computer Science, vol 3004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24652-7_11
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DOI: https://doi.org/10.1007/978-3-540-24652-7_11
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