QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm: A cooperative swarm based algorithm for global optimization

• Published in: Knowledge-based systems Vol. 109; pp. 104 - 121
• Main Authors: Meng, Zhenyu, Pan, Jeng-Shyang, Xu, Huarong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2016
• Subjects: Benchmark functions; Large scale optimization; Particle swarm optimization; QUATRE; Real parameter optimization; State-of-the-art; Benchmark functions; Large scale optimization; Real parameter optimization; Particle swarm optimization; State-of-the-art; QUATRE
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2016.06.029

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.