Multi-objective resource constrained project scheduling problem based on improved ant colony optimization

被引：0
|
作者：
An X. [1 ]
Zhang Z. [2 ]
机构：
[1] Development and Research Institute, Yunnan University, Kunming
[2] Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming
来源：
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2019年 / 39卷 / 02期
关键词：
Ant colony optimization; Local search; Multi-objective optimization; Project scheduling;
D O I：
10.12011/1000-6788-2017-0983-11
中图分类号：
学科分类号：
摘要：
Multi-objective resource constrained project scheduling problem is a typical NP-hard combinational optimization problem with a wide range of application background. In this paper, an improved ant colony optimization with local search is proposed to address the multi-objective resource-constrained project scheduling problem, the aim is to minimize the makespan and resource investment criteria. Firstly, the Pareto sets are obtained by using the improved ant colony optimization (IACO). Secondly, the performance of IACO is enhanced by the logic constraints based local searches, i. e., Insert and Swap, and the non-dominated solutions are further improved. Numerical simulations and comparisons with the state-ofthe- art algorithms based on the international standard benchmarks PSPLIB for MORCPSP are carried out, which demonstrate the effectiveness and efficiency of the proposed algorithm. © 2019, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.
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页码：509 / 519
页数：10
相关论文
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