A Bat Algorithm with Generalized Walk for the Two-Stage Hybrid Flow Shop Problem

In the last years, a set of bio-inspired metaheuristics has proved their efficiencies in combinational and continues optimization areas. This paper intends to hybrid a discrete version of Bat Algorithm (BA) with Generalized Evolutionary Walk Algorithm (GEWA) to solve the mono-processors two stages H...

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Bibliographic Details
Published inInternational journal of decision support system technology Vol. 7; no. 3; pp. 1 - 16
Main Authors Dekhici, Latifa, Belkadi, Khaled
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.07.2015
Subjects
Algorithms
Benchmarking
Decision support systems
Evolutionary
Evolutionary algorithms
Heuristic methods
Job shop scheduling
Mathematical optimization
Optimization
Particle swarm optimization
Scheduling
Swarm intelligence
Algeria
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ISSN1941-6296
1941-630X
DOI10.4018/IJDSST.2015070101

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Summary:In the last years, a set of bio-inspired metaheuristics has proved their efficiencies in combinational and continues optimization areas. This paper intends to hybrid a discrete version of Bat Algorithm (BA) with Generalized Evolutionary Walk Algorithm (GEWA) to solve the mono-processors two stages Hybrid Flow Shop scheduling. The authors compare the modified bat algorithm with the original one, with Particle Swarm Optimization (PSO) and with others results taken from literature. Computational results on a standard two-stage hybrid flow shop benchmark of 70 cases, and about 1700 instances, indicate that the proposed algorithm finds the best makespan (Cmax) in a good processing time comparing to the original bat algorithm and other algorithms.
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ISSN:1941-6296
1941-630X
DOI:10.4018/IJDSST.2015070101