Internal search of the evolution matrix in QUasi-Affine TRansformation Evolution (QUATRE) algorithm

Shu-Chuan Chu, Yuxin Chen, Fanjia Meng, Chen Yang, Jeng-Shyang Pan, Zhenyu Meng

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Optimization demands exist everywhere in the real world especially in science studies and engineering practices, and it is important that the method to deal with intricacy optimization problems should itself be relative simple. Particle Swarm Optimization (PSO) and Differential Evolution (DE) both are simple evolutionary algorithms (EAs) which are proposed for single-objective optimization and both of them have been proved to be efficient methods for optimizing applications, however, there are still some weakness existing within them. A innovative evolutionary method named QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm which is derived from, also tackles some weaknesses of PSO and DE algorithm, and obtains better performance on commonly used test suites. The key characteristic of QUATRE is that an automatically generated matrix named evolution matrix M is implemented in evolutionary process, which is taken as an alternative of employing the crossover rate CR. Here in this paper, we present a novel QUATRE variant, named IS-QUATRE, which can explore the search area in a better way comparison with the previous method, and relatively good optimization ability can be obtained by our proposed IS-QUATRE algorithm under CEC2013 test suit. And the conducted experimental results validate that our proposed IS-QUATRE is competitive with some other famous PSO and DE variants.

Original languageEnglish
Pages (from-to)5673-5684
Number of pages12
JournalJournal of Intelligent and Fuzzy Systems
Issue number5
Publication statusPublished - 2020
Externally publishedYes


  • Internal search
  • real-parameter optimization
  • single-objective optimization
  • stochastic optimization


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