Particle swarm optimization algorithm and its parameters: A review

In the year 1995, Dr R.C. Eberhart, who was an electrical engineer, along with Dr. James Kennedy, a social psycologist invented a random optimization technique which a was later named as Particle Swarm Optimization. As the name itself asserts that this method draws inspiration from natural biotic life of swarms of flocks. It uses the same principle to find most optimal solution to problem in search space as birds do find their most suitable place in a flock or insects do in a swarm. The PSO algorithm is initialized with a horde of particles which are a collection of random feasible solutions. Every single particle in the swarm is initialised a random velocity and as soon as they are assigned a velocity these particles start moving in problem search space. Now from this space the algorithm draws the particle to most suited fitness which in turn pulls it to the location of best fitness achieved across the whole horde. The PSO update rule comprises of many distinguishing features which are adjusted and modified depending upon the area of application of algorithm. This paper gives a detailed description of the PSO algorithm and significance of the various parameters involved in its update rule. It also highlights the advantages and disadvantages of using PSO algorithm in any optimization problem.

[1]  R. Prasad,et al.  Order Reduction of Linear Interval Systems Using Particle Swarm Optimization Devender , 2011 .

[2]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization , 2008, Scholarpedia.

[3]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[4]  Y. Rahmat-Samii,et al.  Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna , 2002, IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No.02CH37313).

[5]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[6]  Yu-Ting Hsiao,et al.  A New Cooperative PSO Approach for the Optimization of Multimodal Functions , 2012 .

[7]  Sudhir Kumar,et al.  Model Order Reduction using Bio-inspired PSO and BFO Soft-Computing for Comparative Study , 2011 .

[8]  M. N. Vrahatis,et al.  Particle swarm optimization method in multiobjective problems , 2002, SAC '02.

[9]  Kalyanmoy Deb,et al.  Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems , 1999, Evolutionary Computation.

[10]  Sidhartha Panda,et al.  Evolutionary Techniques for Model Order Reduction of Large Scale Linear Systems , 2009 .

[11]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[12]  P. Suganthan.  Particle swarm optimiser with neighbourhood operator , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[13]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[14]  Sheela Tiwari,et al.  Reduced Order Modeling of Triple Link Inverted Pendulum Using Particle Swarm Optimization Algorithm , 2014 .

[15]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[16]  Derek A. Linkens,et al.  Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications , 1996 .

[17]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[18]  S. K. Nagar,et al.  Comparative study of Model Order Reduction using combination of PSO with conventional reduction techniques , 2015, 2015 International Conference on Industrial Instrumentation and Control (ICIC).

[19]  Qinghai Bai,et al.  Analysis of Particle Swarm Optimization Algorithm , 2010, Comput. Inf. Sci..

[20]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[21]  Jaroslaw Sobieszczanski-Sobieski,et al.  Particle swarm optimization , 2002 .

[22]  Toshiharu Hatanaka,et al.  Search Performance Improvement for PSO in High Dimensional Space , 2009 .

[23]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.