Abstract
This paper presents a development of a new hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) for solving complex multi-objective optimization problems. In this proposed algorithm, two significant parameters such as crossover probability (P C) and mutation probability (P M) are dynamically varied during optimization based on the output of a fuzzy controller for improving its convergence performance by guiding the direction of stochastic search to reach near the true pareto-optimal solution effectively. The performance of HFMOEA is examined and compared with NSGA-II on three benchmark test problems such as ZDT1, ZDT2 and ZDT3.
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References
Srinivas, N., Deb, K.: Multi-objective Optimization Using Non-dominated Sorting in Genetic Algorithms. Evolutionary Computation 2(3), 221–248 (1994)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)
Knowles, J., Corne, D.: The Pareto archived evolution strategy: A new baseline algorithm for multi-objective optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 98–105. IEEE Press, Piscataway (1999)
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: Methods and applications, Doctoral dissertation ETH 13398. Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (1999)
Raghuwanshi, M.M., Kakde, O.G.: Survey on multi-objective evolutionary and real coded genetic algorithms. In: Proceedings of the 8th Asia Pacific Symposium on Intelligent and Evolutionary Systems, pp. 150–161 (2004)
Saraswat, A., Saini, A.: Optimal reactive power dispatch by an improved real coded genetic algorithm with PCA mutation. In: Proceedings of Second International Conference on Sustainable Energy and Intelligent System (IET SEISCON 2011), vol. 2, pp. 310–315 (2011)
Dhillon, J.S., Parti, S.C., Kothari, D.P.: Stochastic economic emission load dispatch. Electric Power Systems Research 26, 179–186 (1993)
Abido, M.A., Bakhashwain, J.M.: Optimal VAR dispatch using a multi-objective evolutionary algorithm. Electric Power and Energy Systems 27(1), 13–20 (2005)
Saini, A., Chaturvedi, D.K., Saxena, A.K.: Optimal power flow solution: a GA-Fuzzy system approach. International Journal of Emerging Electric Power Systems 5(2) (2006)
Deb, K., Agarwal, R.B.: Simulated Binary Crossover for Continuous Search Space. Complex Systems 9, 115–148 (1995)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multi-objective evolutionary algorithms: Empirical results. Evolutionary Computing 8(2), 173–195 (2000)
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Saraswat, A., Saini, A. (2012). A Novel Hybrid Fuzzy Multi-Objective Evolutionary Algorithm: HFMOEA. In: Meghanathan, N., Chaki, N., Nagamalai, D. (eds) Advances in Computer Science and Information Technology. Computer Science and Information Technology. CCSIT 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27317-9_18
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DOI: https://doi.org/10.1007/978-3-642-27317-9_18
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