Two Phase Sub-Population Genetic Algorithm for Parallel Machine Scheduling problem

doi:10.1016/j.eswa.2005.04.033

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Title:	Two Phase Sub-Population Genetic Algorithm for Parallel Machine Scheduling problem
Authors:	Chang, Pei-Chann;Chen, Shih-Hsin;Lin, Kun-Lin
Keywords:	Scheduling problem;Genetic algorithm;Multi-objective optimization;Evolution strategy
Date:	2005-10
Issue Date:	2021-10-07 12:10:46 (UTC+8)
Publisher:	Elsevier Ltd
Abstract:	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. Sub-population 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.
Relation:	Expert Systems with Applications 29(3), p.705-712
DOI:	10.1016/j.eswa.2005.04.033
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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