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Two-phase sub population genetic algorithm for parallel machine-scheduling problem
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DOI：10.1016/j.eswa.2005.04.033.png)
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Abstract
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This paper introduces a two-phase sub population genetic algorithm to solve the parallel machine-scheduling problem. In the first phase, the population will be decomposed into many sub-populations and each sub-population is designed for a scalar multi-objective. Subpopulation is a new approach for solving multi-objective problems by fixing each sub-population for a pre-determined criterion. In the second phase, non-dominant solutions will be combined after the first phase and all sub-population will be unified as one big population. Not only the algorithm merges sub-populations but the external memory of Pareto solution is also merged and updated. Then, one unified population with each chromosome search for a specific weighted objective during the next evolution process. The two phase sub-population genetic algorithm is applied to solve the parallel machine-scheduling problems in testing of the efficiency and efficacy. Experimental results are reported and the superiority of this approach is discussed. (c) 2005 Elsevier Ltd. All rights reserved.
Keywords:
scheduling problem
genetic algorithm
multi-objective optimization
evolution strategy
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