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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© 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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