In this paper, a hybrid algorithm, based on clonal selection algorithm (CSA) and small population based particle swarm optimization (SPPSO) is introduced. The performance of this new algorithm (CS2P2SO) is observed for four well known benchmark functions. The SPPSO is a variant of conventional PSO (CPSO), introduced by the second author of this paper, where a very small number of initial particles are used and after a few iterations, the best particle is kept and the rest are replaced by the same number of regenerated particles. On the other hand, CSA belongs to the family of artificial immune system (AIS). It is an evolutionary algorithm, where, during evolution, the antibodies which can recognize the antigens proliferate by cloning. With the hybridization of these two algorithms, the strength of CPSO is enhanced to a great extent. The concept of SPPSO helps to find the optimum solution with less memory requirement and the concept of CSA increases the exploration capability and reduces the chances of convergence to local minima. The test results show that CS2P2SO performs better than CPSO and SPPSO for the Sphere, Rosenbrockpsilas, Rastriginpsilas and Griewankpsilas functions.
P. Mitra and G. K. Venayagamoorthy, "Empirical Study of a Hybrid Algorithm Based on Clonal Selection and Small Population Based PSO.," Proceedings of the 2008 IEEE Swarm Intelligence Symposium (2008, St. Louis, MO), Institute of Electrical and Electronics Engineers (IEEE), Sep 2008.
The definitive version is available at https://doi.org/10.1109/SIS.2008.4668329
2008 IEEE Swarm Intelligence Symposium (2008, St. Louis, MO)
Electrical and Computer Engineering
National Science Foundation (U.S.)
Keywords and Phrases
Artificial Immune Systems; Evolutionary Computation; Particle Swarm Optimisation
Article - Conference proceedings
© 2008 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Sep 2008