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)


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.


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


  1. 1.
    Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Trans. Evol. Comput. 3(4), 287–297 (1999)CrossRefGoogle Scholar
  2. 2.
    Mininno, E., Cupertino, F., Naso, D.: Real-valued compact genetic algorithms for embedded microcontroller optimization. IEEE Trans. Evol. Comput. 12(2), 203–219 (2008)CrossRefGoogle Scholar
  3. 3.
    Mininno, E., Neri, F., Cupertino, F., Naso, D.: Compact differential evolution. IEEE Trans. Evol. Comput. 15(1), 32–54 (2011)CrossRefGoogle Scholar
  4. 4.
    Iacca, G., Neri, F., Mininno, E.: Opposition-based learning in compact differential evolution. In: Evo Applications 2011 Part I, Lecture Notes in Computer Science, vol. 6624, pp. 264–273. Springer (2011)Google Scholar
  5. 5.
    Neri, F., Mininno, E., Iacca, G.: Compact particle swarm optimization. Inf. Sci. 239, 96–121 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  7. 7.
    Chu, S.C., Tsai, P.W., Pan, J.S.: Cat swarm optimization. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence, pp. 854–858 (2006)Google Scholar
  8. 8.
    Tsai, P.-W., Pan, J.-S., Chen, S.-M., Liao, B.-Y.: Enhanced parallel cat swarm optimization based on the Taguchi method. Expert Syst. Appl. 39(7), 6309–6319 (2012)CrossRefGoogle Scholar
  9. 9.
    Pradhan, P.M., Panda, G.: Solving multi objective problems using cat swarm optimization. Expert Syst. Appl. 39(3), 2956–2964 (2012)CrossRefGoogle Scholar
  10. 10.
    Wang, Z.-H., Chang, C.-C., Li, M.-C.: Optimizing least-significant-bit substitution using cat swarm. Inf. Sci. 192(1), 98–108 (2012)CrossRefGoogle Scholar
  11. 11.
    Jung, M.-J., Myung, H., Lee, H.-K., Bang, S.: Ambiguity resolving in structured light 2D range finder for SLAM operation for home robot applications. In: Proceedings of the IEEE Workshop on Advanced Robotics and its Social Impacts, pp. 18–23 (2005)Google Scholar
  12. 12.
    Okazaki, A., Senoo, T., Imae, J., Kobayashi, T., Zhai, G.: Real-time optimization for cleaner-robot with multi-joint arm. In: Proceedings of the International Conference on Networking, Sensing and Control, pp. 885–890 (2009)Google Scholar
  13. 13.
    Gautschi, W.: Error function and fresnel integrals, In: Abramowitz, M., Stegun, I.A. (eds.) Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, pp. 297–309 (1972)Google Scholar
  14. 14.
    Cody, W.J.: Rational Chebyshev approximations for the error function. Math. Comput. 23(107), 631–637 (1969)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Neri, F., Cotta, C., Moscato, P.: Handbook of Memetic Algorithms, Studies in Computational Intelligence, vol. 379. Springer, Berlin Heidelberg (2011)Google Scholar
  16. 16.
    van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)CrossRefGoogle Scholar
  17. 17.
    Tang, K., Yao, X., Suganthan, P.N., MacNish, C., Chen, Y.P., Chen, C.M., Yang, Z.: Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Technical reportGoogle Scholar
  18. 18.
    Pedersen, M.E.H.: Good parameters for particle swarm optimization. Technical report HL1001, Hvass Lab. (2010)Google Scholar

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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