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Compact Cat Swarm Optimization Algorithm

  • Ming Zhao
  • Jeng-Shyang Pan
  • Shuo-Tsung Chen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 733)

Abstract

A compact cat swarm optimization algorithm (cCSO) was proposed in this paper. it keeps the same search logic of cat swarm optimization (CSO), i.e. tracing mode and seeking mode, on the other hands, cCSO inherits the main feature of compact optimization algorithms, a normal probabilistic vector is used to generate new individuals, the mean and the standard deviation of the probabilistic model could lead cats to the searching direction in next step. Only a cat is adopted in the algorithm, thus, it could run with modest memory requirement. Experimental results show that cCSO has better performance than some compact optimization algorithms in some benchmark functions test. The convergence rate is also a highlight among compact optimization algorithms.

Keywords

Compact optimization Cat swarm optimization Normal probabilistic model Memory saving Differential factor 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceYangtze UniversityJingzhouChina
  2. 2.School of Information EngineeringFuzhou University of TechnologyFuzhouChina
  3. 3.Department of MathematicTung Hai UniversityTaichungTaiwan

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