求解PFSP 的双种群协同学习算法
Double population co-learning algorithm for permutation flow-shop scheduling problems
查看参考文献15篇
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文摘
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在人工蜜蜂群算法的基础上, 提出一种双种群协同学习算法. 该算法根据个体适应度高低把蜜蜂群划分为两个子群, 并重新定义子群的学习交流机制. 在10个常用的基准测试函数上与其他4个常用的群体智能算法进行比较, 比较结果表明, 所提出算法的性能有明显改进. 采用双种群协同学习算法求解置换流水车间调度问题, 在一些著名的中大规模测试问题包括21个Reeves 实例和40个Taillard 实例上进行测试, 结果表明, 所提出的算法优于其他算法, 能有效解决置换流水车间调度问题. |
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其他语种文摘
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Based on the artificial bee colony(ABC) algorithm, a double population co-learning(DPCL) algorithm is proposed. A population is divided into two populations according to their fitness. The individuals of each population are updated according to the given learning rules. With a test on ten benchmark functions, the proposed DPCL algorithm is proved to have significant improvement over canonical ABC and several other comparison algorithms. The DPCL algorithm is then employed for permutation flow-shop scheduling problem(PFSP). Twenty-one Reeves instances and forty Taillard instances are used. The results show that the DPCL algorithm can obtain better results than other algorithms, and is a competitive approach for PFSP. |
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来源
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控制与决策
,2017,32(1):12-20 【核心库】
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DOI
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10.13195/j.kzyjc.2015.1568
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关键词
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协同学习
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置换流水车间调度
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智能算法
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地址
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中国科学院沈阳自动化研究所, 沈阳, 110016
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1001-0920 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金国家杰出青年科学基金
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辽宁省自然科学基金
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文献收藏号
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CSCD:5926497
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参考文献 共
15
共1页
|
|
1.
Kennedy J. Particle swarm optimization.
IEEE Int Conf Neural Networks,1995:1942-1945
|
被引
5
次
|
|
|
|
|
2.
宋存利. 求解车间调度问题的2阶段混合粒子群优化算法.
信息与控制,2012,41(2):193-196
|
被引
4
次
|
|
|
|
|
3.
Nawaz M. A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem.
Omega-Int J of Management Science,1983,11(1):91-95
|
被引
149
次
|
|
|
|
|
4.
刘延风. 改进微粒群优化求解置换流水车间调度问题.
计算机集成制造系统,2009,15(10):1968-1972
|
被引
7
次
|
|
|
|
|
5.
Tasgetiren M F. Particle swarm optimization algorithm for permutation flow shop sequencing problem.
Lecture Notes in Computer Science,2004,3172:382-389
|
被引
8
次
|
|
|
|
|
6.
Tasgetiren M F. A particle swarm optimization algorithm for makespan and total flow time minimization in the permutation flow shop sequencing problem.
European J of Operational Research,2007,77(3):1930-1947
|
被引
27
次
|
|
|
|
|
7.
Karaboga D.
An idea based on honey bees warm for numerical optimization,2005
|
被引
2
次
|
|
|
|
|
8.
Tasgetiren M F. A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops.
Information Sciences,2011,181(16):3459-3475
|
被引
13
次
|
|
|
|
|
9.
晏晓辉.
群体智能算法研究及其在调度优化中的应用,2012
|
被引
1
次
|
|
|
|
|
10.
Xie Z P. An effective hybrid teaching-learning-based optimization algorithm for permutation flow shop scheduling problem.
Advances in Engineering Software,2014,77:35-47
|
被引
6
次
|
|
|
|
|
11.
Niu Bei. MCPSO: A multi-swarm cooperative particle swarm optimizer.
Applied Mathematics and Computation,2007,185(2):1050-1062
|
被引
21
次
|
|
|
|
|
12.
Karaboga D. A comparative study of artificial bee colony algorithm.
Applied Mathematics and Computation,2009,214(1):108-132
|
被引
246
次
|
|
|
|
|
13.
Van Den Bergh F. A cooperative approach to participle swam optimization.
IEEE Trans on Evolutionary Computation,2004,8(3):225-239
|
被引
194
次
|
|
|
|
|
14.
Zhang Y. Hybrid genetic algorithm for permutation flowshop scheduling problems with total flowtime minimization.
European J of Operational Research,2009,196(3):869-876
|
被引
8
次
|
|
|
|
|
15.
Wang L. A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems.
Computers & Operations Research,2010,37(3):509-520
|
被引
23
次
|
|
|
|
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