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Cellular Genetic Algorithm with Communicating Grids for Assembly Line Balancing ProblemsBRUDARU, O. , POPOVICI, D. , COPACEANU, C.
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cellular genetic algorithms, communicating grids, I/U-shaped assembly lines, parallel workstations, and task compatibility constraints
link(7), genetic(6), balancing(5), assembly(5), algorithms(5), line(4)
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About this article
Date of Publication: 2010-05-31
Volume 10, Issue 2, Year 2010, On page(s): 87 - 93
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.02015
Web of Science Accession Number: 000280312600015
SCOPUS ID: 77954656030
This paper presents a new approach with cellular multigrid genetic algorithms for the I-shaped and U-shaped assembly line balancing problems, including parallel workstations and compatibility constraints. First, a cellular hybrid genetic algorithm that uses a single grid is described. Appropriate operators for mutation, hypermutation, and crossover and two devoration techniques are proposed for creating and maintaining groups based on similarity. This monogrid algorithm is extended for handling many populations placed on different grids. In the multigrid version, the population of each grid is organized in clusters using the positional information of the chromosomes. A similarity preserving communication protocol between the clusters placed on different grids is introduced. The experimental evaluation shows that the multigrid cellular genetic algorithm with communicating grids is better than the hybrid genetic algorithm used for building it, whereas it dominates the monogrid version in all cases. Absolute performance is evaluated using classical benchmarks. The role of certain components of the cellular algorithm is explained and the effect of some parameters is evaluated.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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