Research Article
A Novel Hybrid Fuzzy Multi-Objective Evolutionary Algorithm: HFMOEA
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@INPROCEEDINGS{10.1007/978-3-642-27317-9_18, author={Amit Saraswat and Ashish Saini}, title={A Novel Hybrid Fuzzy Multi-Objective Evolutionary Algorithm: HFMOEA}, proceedings={Advances in Computer Science and Information Technology. Computer Science and Information Technology. Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part III}, proceedings_a={CCSIT PART III}, year={2012}, month={11}, keywords={Multi-objective evolutionary algorithms fuzzy logic controller global optimal solution pareto-optimal front}, doi={10.1007/978-3-642-27317-9_18} }- Amit Saraswat
Ashish Saini
Year: 2012
A Novel Hybrid Fuzzy Multi-Objective Evolutionary Algorithm: HFMOEA
CCSIT PART III
Springer
DOI: 10.1007/978-3-642-27317-9_18
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 ( ) and mutation probability ( ) 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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