Modified Teaching-Learning Based Optimization for 0-1 knapsack Optimization Problems

被引：0
|
作者：
Ouyang, Haibin [1 ]
Wang, Qing [1 ]
Kong, Xiangyong [2 ]
机构：
[1] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Guangdong, Peoples R China
[2] Jiangsu Normal Univ, Sch Elect Engn & Automat, Xuzhou 221116, Peoples R China
来源：
2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年
关键词：
Teaching-learning-based optimization; 0-1 knapsack optimization problems; estimation of distribution operation; global learning operation; ALGORITHM;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this article, a modified teaching-learning-based optimization (MTLBO) algorithm is proposed for solving 0-1 knapsack optimization problems. The MTLBO incorporated estimation of distribution operation into teaching phases, which aims at reducing the possibility of premature and predicting an elite teacher. A stochastic local exploitation is used in teaching phase for improving local searching capability. Moreover, in the learning phase, a new global learning operation is presented to boost learning efficiency. Several classic 0-1 knapsack cases are selected to evaluate the performance of MTLBO. Numerical results reveal that the proposed algorithm surpasses TLBO and several other promising heuristic methods.
引用
收藏
页码：973 / 977
页数：5
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