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     Research Journal of Applied Sciences, Engineering and Technology

    Abstract
2015(Vol.9, Issue:9)
Article Information:

A Hybrid Evolutionary Algorithm for Discrete Optimization

J. Bhuvana
Corresponding Author:  J. Bhuvana 
Submitted: ‎October 10, ‎2014
Accepted: November ‎3, ‎2014
Published: March 25, 2015
Abstract:
Most of the real world multi-objective problems demand us to choose one Pareto optimal solution out of a finite set of choices. Flexible job shop scheduling problem is one such problem whose solutions are required to be selected from a discrete solution space. In this study we have designed a hybrid genetic algorithm to solve this scheduling problem. Hybrid genetic algorithms combine both the aspects of the search, exploration and exploitation of the search space. Proposed algorithm, Hybrid GA with Discrete Local Search, performs global search through the GA and exploits the locality through discrete local search. Proposed hybrid algorithm not only has the ability to generate Pareto optimal solutions and also identifies them with less computation. Five different benchmark test instances are used to evaluate the performance of the proposed algorithm. Results observed shown that the proposed algorithm has produced the known Pareto optimal solutions through exploration and exploitation of the search space with less number of functional evaluations.

Key words:  Discrete local search, flexible job shop scheduling, genetic algorithm, memetic algorithm, multi-objective optimization, Pareto optimal,
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Cite this Reference:
J. Bhuvana, . A Hybrid Evolutionary Algorithm for Discrete Optimization . Research Journal of Applied Sciences, Engineering and Technology, (9): 770-777.
ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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