Journal
INFORMATION SCIENCES
Volume 352, Issue -, Pages 61-78Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.02.054
Keywords
Teaching-learning-based optimization; Constrained optimization; Constraint handling; Learning strategy
Categories
Funding
- National Key Scientific and Technical Project of China [2015BAF22B02]
- National Natural Science Foundation of China [61403141]
- Shanghai Natural Science Foundation [14ZR1421800]
- State Key Laboratory of Synthetical Automation for Process Industries [PAL-N201404]
Ask authors/readers for more resources
This paper proposes an improved constrained teaching-learning-based optimization (ICTLBO) method to efficiently solve constrained optimization problems (COPs). In the teacher phase of ICTLBO, the population is partitioned into several subpopulations, and the direction information between the mean position of each subpopulation and the best position of population guide the corresponding subpopulation to the promising region promptly. Information exchange between different subpopulations is used to discourage premature convergence of each subpopulation. Furthermore, in the learner phase, a new learning strategy is introduced to improve the population diversity and enhance the global search ability. Three different constraint handling methods are adopted for three situations, which are infeasible, semi-feasible, and feasible situations, during the evolution process. To evaluate the performance of ICTLBO, 22 benchmark functions presented in CEC2006 and 18 benchmark functions introduced in CEC2010 are chosen as the test suite. Moreover, four widely used engineering design problems are selected to test the performance of ICTLBO for real-world problems. Experimental results indicate that ICTLBO can obtain a highly competitive performance compared with other state-of-the-art algorithms. (C) 2016 Elsevier Inc. All rights reserved.Inc
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available