Abstract
A new fitness function is proposed in the paper at first, in which the fitness of an individual is defined by the maximum value of the weighted normalized objectives. In order to get the required weights, the sphere coordinate transformation is used. The fitness constructed in this way can result in a group of uniform search directions in the objective space. By using these search directions, the evolutionary algorithm can explore the objective space uniformly, keep the diversity of the population and find uniformly distributed solutions on the Pareto frontier gradually. The numerical simulations indicate the proposed algorithm is efficient and has a better performance than the compared ones.
This work was supported in part by the Natural Science Foundation of Shaanxi Province (No. 2001SL06), by the SRF for ROCS, SEM, and by the Doctoral Fund of Guangdong University of Technology.
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References
Leung, Y.W., Wang, Y.P.: Multiobjective programming using uniform design and genetic algorithm. IEEE Trans. on Syst., Man, Cybern. C 30, 293–304 (2000)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. on Evolutionary Computation 3, 257–271 (1999)
Ishibuchi, H., Murata, T.: A multiobjective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans. on Syst., Man, Cybern. C 28, 392–403 (1998)
Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraints handling with evolutionary algorithms-Part 1: Unified formulation. IEEE Trans. on Syst., Man, Cybern. A 28, 38–47 (1998)
Hajela, P., Lin, C.Y.: Genetic search strategies in multicriterion optimal design. Structural Optimization 4, 99–107 (1992)
Deb, K.: Multiobjective genetic algorithms: Problem difficulties and construction of test functions Evolutionary Computation 7, 205–230 (1999)
Fang, F.T., Wang, Y.: Number-theoretic methods in statistics. Chapman & Hall, London (1994)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8, 173–195 (2000)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) Proceedings of the Parallel Problem Solving from Nature VI Conference. Springer, Heidelberg (2000)
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Liu, H.L., Wang, Y. (2003). A Novel Multiobjective Evolutionary Algorithm Based on Min-Max Strategy. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_47
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DOI: https://doi.org/10.1007/978-3-540-45080-1_47
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