Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10323547 | Expert Systems with Applications | 2005 | 8 Pages |
Abstract
This paper introduces a twoâphase 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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Pei-Chann Chang, Shih-Hsin Chen, Kun-Lin Lin,