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A cross-entropy-based population-learning algorithm for discrete-continuous scheduling with continuous resource discretisation

引用

NEUROCOMPUTING 2010年第4-6期73卷 655-660页

作者： Jedrzejowicz, Piotr Skakovski, Aleksander Gdynia Maritime Univ Dept Nav PL-81225 Gdynia Poland Gdynia Maritime Univ Chair Informat Syst PL-81225 Gdynia Poland

The problem of scheduling nonpreemtable tasks on parallel identical machines under constraint on discrete resource and requiring, additionally, renewable continuous resource to minimize the schedule length is considered in the paper. A continuous resource is divisible continuously and is allocated to tasks from given intervals in amounts unknown in advance. Task processing rate depends on the allocated amount of the continuous resource. To eliminate time-consuming optimal continuous resource allocation, a problem Theta(Z) with continuous resource discretisation is introduced. Because Theta(Z) is NP-hard a population-learning algorithm (PLA2) is proposed to tackle the problem. PLA2 is a population-based approach which takes advantage of the features common to the social education system rather than to the evolutionary processes. The proposed approach is based on the idea of constructing the hybrid algorithm integrating different optimization techniques complementing each other and producing a synergetic effect. Experimental results proved that PLA2 excels known algorithms for solving the considered problem. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Discrete-continuous scheduling Evolutionary computation population-learning algorithm

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A cross-entropy-based population-learning algorithm for discrete-continuous scheduling with continuous resource discretisation

引用

12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems

作者： Jedrzejowicz, Piotr Skakovski, Aleksander Gdynia Maritime Univ Dept Nav PL-81225 Gdynia Poland Gdynia Maritime Univ Chair Informat Syst PL-81225 Gdynia Poland

ISBN: (纸本)3540855629

关键词： Discrete-continuous scheduling Evolutionary computation population-learning algorithm

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