期刊
KNOWLEDGE-BASED SYSTEMS
卷 109, 期 -, 页码 104-121出版社
ELSEVIER
DOI: 10.1016/j.knosys.2016.06.029
关键词
Benchmark functions; Large scale optimization; Particle swarm optimization; QUATRE; Real parameter optimization; State-of-the-art
资金
- National Natural Science Foundation of China [61273290]
This paper presents a new novel evolutionary approach named Quasi-Affine TRansformation Evolutionary (QUATRE) algorithm, which is a swarm based algorithm and use quasi-affine transformation approach for evolution. The paper also discusses the relation between QUATRE algorithm and other kinds of swarm based algorithms including Particle Swarm Optimization (PSO) variants and Differential Evolution (DE) variants. Comparisons and contrasts are made among the proposed QUATRE algorithm, state-of-the-art PSO variants and DE variants under CEC2013 test suite on real-parameter optimization and CEC2008 test suite on large-scale optimization. Experiment results show that our algorithm outperforms the other algorithms not only on real-parameter optimization but also on large-scale optimization. Moreover, our algorithm has a much more cooperative property that to some extent it can reduce the time complexity (better performance can be achieved by reducing number of generations required for a target optimum by increasing particle population size with the total number of function evaluations unchanged). In general, the proposed algorithm has excellent performance not only on uni-modal functions, but also on multi-modal functions even on higher dimension optimization problems. (C) 2016 Elsevier B.V. All rights reserved.
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