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Computation model and improved ACO algorithm for p//T
Authors:Yi Yang  Lai Jieling
Institution:School of Information Science & Technology, Sun Yat-sen Univ., Guangzhou 510275, P.R.China
Abstract:Scheduling jobs on parallel machines to minimize the total tardiness (p//T) is proved to be NP hard. A new ant colony algorithm to deal with p//T (p//T ACO) is addressed, and the computing model of mapping p//T to the ant colony optimization environment is designed. Besides, based on the academic researches on p//T, some new properties used in the evolutionary computation are analyzed and proved. The theoretical analysis and comparative experiments demonstrate that p//T ACO has much better performance and can be used to solve practical large scale problems efficiently.
Keywords:scheduling  evolutionary computation  ant colony optimization
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