• P. Rajapandian

Particle Swarm Optimization (PSO) is one of the most popular population-based swarm intelligent algorithms inspired by the behaviours of birds flocking and fish schooling. This algorithm holds few tuning parameters to obtain the optimum solution; however, PSO has maximum possibility to stagnate in local optima due to its simplicity and flexibility. Several modified PSO versions are developed to maintain the diversity of the particles and to avoid the local optima struck. In this paper, Generic PSO and a novel collection of eight different PSO variants are presented. The collected PSO variants have successful balancing capabilities between the local search and global search of optimum solutions. This survey offers good understanding on generic particle swarm optimization and its distinct variants. The particles in each algorithm have unique characteristics on converging towards best optimum solutions. The experimentation results of the collected PSO variants are processed using MATLAB (version 2013 a). The performance of distinct collection of PSO variants are computed using three different groups of benchmark test function viz., unimodal, multimodal and fixed dimension multimodal functions. The performance of each algorithm is discussed with the experimentation results.

Particle Swarm Optimization (PSO); Swarm Intelligent; unimodal; multimodal; fixed dimension multimodal function.

"A comprehensive survey on Particle Swarm Optimization (PSO) and its variants to classical benchmark functions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.886-901, June 2019, Available :http://www.jetir.org/papers/JETIR1906G36.pdf

"A comprehensive survey on Particle Swarm Optimization (PSO) and its variants to classical benchmark 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. pp886-901, June 2019, Available at : http://www.jetir.org/papers/JETIR1906G36.pdf