• Shreya Sood
• Dr. Arvind Kalia
• Naveen Kumar

Abstract Particle Swarm Optimisation is used for solving non-linear functions. It is simpler than any other evolutionary techniques as it is easy to implement by means of basic mathematical and logic operations. It is a population-based stochastic optimisation technique modelled on the social behaviour observed in flock of bird, school of fish, etc. Test functions hold some diverse properties which helps to find the efficiency, reliability and performance of optimisation algorithms. A good set of test functions used on any new optimisation, helps to validate its performance and compare it with other existing algorithms. These functions are well suited to evaluate the algorithm by comparing its efficiency with other algorithms and test its validity using different parameters. Only few of the test functions have been applied to the particle swarm optimisation algorithm earlier. More test functions applied on the particle swarm optimisation algorithm will help to see the algorithm in vibrant prospect and improvement in its performance. In this paper twenty two test functions have been applied on the particle swarm optimisation algorithm.

Keywords: Optimisation, Optimisation Algorithms, Optimum, Particle Swarm Optimisation, Search Space, Swarm, Test Functions Variants

"Evaluating the Performance of Particle Swarm Optimisation Algorithm using Some Single Objective Unconstrained Functions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.606-612, June-2019, Available :http://www.jetir.org/papers/JETIR1906E41.pdf

"Evaluating the Performance of Particle Swarm Optimisation Algorithm using Some Single Objective Unconstrained Functions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp606-612, June-2019, Available at : http://www.jetir.org/papers/JETIR1906E41.pdf