PSO Based Memetic Algorithm for Unimodal and Multimodal Function Optimization

  • Swapna Devi
  • Devidas G. Jadhav
  • Shyam S. Pattnaik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)


Memetic Algorithm is a metaheuristic search method. It is based on both the natural evolution and individual learning by transmitting unit of information among them. In the present paper, Genetic Algorithm due to its good exploration capability is used for exploration and Particle Swarm Optimization (PSO) does local search. The memetic process is realized using the fitness information from the individual having best fitness value and searching around it locally with PSO. The proposed algorithm (PSO based memetic algorithm -pMA) is tested on 13 standard benchmark functions having unimodal and multimodal property. When results are compared, the proposed memetic algorithm shows better performance than GA and PSO. The performance of the discussed memetic algorithm is better in terms of convergence speed and quality of solutions.


Genetic Algorithm Local Search Memetic Algorithm Local Search Algorithm Multimodal Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nguyen, Q.H., Ong, Y.S., Krasnogor, N.: A Study on the Design Issues of Me-metic Algorithm. In: Proc. of the IEEE Congr. Evol. Comput. (CEC 2007), pp. 2390–2397 (September 2007)Google Scholar
  2. 2.
    Moscato, P.A.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms, Tech. Rep. Caltech Concurrent Computation Program, California Institute of Technology, Pasadena, CA, Report 826 (1989)Google Scholar
  3. 3.
    Lozano, M., Herrera, F., Krasnogor, N., Molina, D.: Real-Coded Memetic Algo-rithms with Crossover Hill-Climbing. Evolutionary Computation 12(3), 273–302 (2004)CrossRefGoogle Scholar
  4. 4.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)CrossRefzbMATHGoogle Scholar
  5. 5.
    Das, S., Suganthan, P.N.: Differential evolution - a survey of the state-of-the-art. IEEE Trans. on Evolutionary Computation 15(1), 4–31 (2011)CrossRefGoogle Scholar
  6. 6.
    Yao, X., Liu, Y., Lin, G.: Evolutionary Programming Made Faster. IEEE Trans. on Evolutionary Computation 3(2), 82–102 (1999)CrossRefGoogle Scholar
  7. 7.
    Akbari, R., Ziarati, K.: Combination of Particle Swarm Optimization and Stochastic Local Search for Multimodal Function Optimization. In: Proc. of the IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA 2008), pp. 388–392 (2008)Google Scholar
  8. 8.
    Li, B., Ong, Y.S., Le M.N., Goh, C.K.: Memetic Gradient Search. In: Proc. of the IEEE Congress on Evol. Comput. (CEC 2008), pp. 2894–2901 (2008)Google Scholar
  9. 9.
    Jadhav, D.G., Pattnaik, S.S., Devi, S., Lohokare, M.R., Bakwad, K.M.: Approximate Memetic Algorithm for Consistent Convergence. In: Proc. National Conf. on Computational Instrumentation (NCCI 2010), pp. 118–122 (March 2010)Google Scholar
  10. 10.
    Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-shemata. In: Darrell Whitley, L. (ed.) Foundation of Genetic Algorithms, vol. 2, pp. 187–202. Morgan Kaufmann, San Mateo (1993)Google Scholar
  11. 11.
    Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9(2), 115–148 (1995)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions. IEEE Trans. on Evol. Comput. 10(3), 281–295 (2006)CrossRefGoogle Scholar
  13. 13.
    Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Technical Report, Nanyang Technological University, Singapore, & KanGAL Report #2005005, IIT Kanpur, India (May 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Swapna Devi
    • 1
  • Devidas G. Jadhav
    • 1
  • Shyam S. Pattnaik
    • 1
  1. 1.National Institute of Technical Teachers’ Training & Research (NITTTR), Sector-26ChandigarhIndia

Personalised recommendations