Publication Type:

Conference Paper


2014 International Conference on Signal Processing and Integrated Networks, SPIN 2014, IEEE Computer Society, Noida, p.20-24 (2014)





Algorithms, Artificial intelligence, Global optimization, Iterative methods, Mutation, Number of iterations, Particle swarm optimization (PSO), Particle swarm optimization algorithm, Performance parameters, Predefined precision, PSO, Signal processing, Swarm convergence, Swarm Intelligence


The improvised Particle Swarm Optimization (PSO) Algorithm offers better search efficiency than conventional PSO algorithm. It provides an efficient technique to obtain the best optimized result in the search space. This algorithm ensures a faster rate of convergence to the desired solution whose precision can be preset by the user. The inertia parameter is varied linearly with iteration number, which results in more accurate solution for unimodal functions. The control over the precision value acts as a trade-off between the convergence time and precision of the desired solution, and it can be viewed as a performance parameter. Swarm convergence is followed by a mutation process, which further improves the obtained result by enhancing the local search ability of some particles. The results show that the solution with predefined precision level can be obtained with the minimum number of iterations. © 2014 IEEE.


cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@39a52b4d ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@abde9cc Through org.apache.xalan.xsltc.dom.DOMAdapter@3a405596; Conference Code:105684

Cite this Research Publication

B. Anand, Aakash, I., ,, Varrun, V., Reddy, M. K., Sathyasai, T., and M. Nirmala Devi, “Improvisation of particle swarm optimization algorithm”, in 2014 International Conference on Signal Processing and Integrated Networks, SPIN 2014, Noida, 2014, pp. 20-24.