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Cellular Genetic Algorithm with Communicating Grids for Assembly Line Balancing ProblemsBRUDARU, O. , POPOVICI, D. , COPACEANU, C. |
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Author keywords
cellular genetic algorithms, communicating grids, I/U-shaped assembly lines, parallel workstations, and task compatibility constraints
References keywords
link(7), genetic(6), balancing(5), assembly(5), algorithms(5), line(4)
Blue keywords are present in both the references section and the paper title.
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
Abstract
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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[1] Improvement of the Gravitational Search Algorithm by means of Low-Discrepancy Sobol Quasi Random-Number Sequence Based Initialization, ALTINOZ, O. T., YILMAZ, A. E., WEBER, G.-W., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 14, 2014.
Digital Object Identifier: 10.4316/AECE.2014.03007 [CrossRef] [Full text]
[2] A taxonomy of line balancing problems and their solutionapproaches, Battaïa, Olga, Dolgui, Alexandre, International Journal of Production Economics, ISSN 0925-5273, Issue 2, Volume 142, 2013.
Digital Object Identifier: 10.1016/j.ijpe.2012.10.020 [CrossRef]
[3] A Combined Methodology of Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm for Short-term Energy Forecasting, KAMPOUROPOULOS, K., ANDRADE, F., GARCIA, A., ROMERAL, L., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 14, 2014.
Digital Object Identifier: 10.4316/AECE.2014.01002 [CrossRef] [Full text]
[4] Cellular Genetic Algorithm with Communicating Grids for a Delivery Problem, Brudaru, Octav, Vilcu, Adrian, Popovici, Diana, 2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, ISBN 978-1-4673-0207-4, 2011.
Digital Object Identifier: 10.1109/SYNASC.2011.58 [CrossRef]
[5] Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs, Rotaru, Cristian, Brudaru, Octav, 2012 IEEE Congress on Evolutionary Computation, ISBN 978-1-4673-1509-8, 2012.
Digital Object Identifier: 10.1109/CEC.2012.6256590 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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