A Novel Hybrid Fuzzy Multi-Objective Evolutionary Algorithm: HFMOEA

Amit Saraswat; Ashish Saini

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

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

Amit Saraswat¹^,*, Ashish Saini¹^,*

1: Dayalbagh Educational Institute

*Contact email: amitsaras@gmail.com, ashish_711@rediffmail.com

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.

Keywords: Multi-objective evolutionary algorithms fuzzy logic controller global optimal solution pareto-optimal front

Published: 2012-11-09

: http://dx.doi.org/10.1007/978-3-642-27317-9_18