Paper Info
Title
A New Particle Acceleration-Based Particle Swarm Optimization Algorithm.
Abstract
Optimization of one or more objective function is a requirement for many real life problems. Due to their wide applicability in business, engineering and other areas, a number of algorithms have been proposed in literature to solve these problems to get optimal solutions in minimum possible time. Particle Swarm Optimization (PSO) is a very popular optimization algorithm, and was developed by Dr. James Kennedy and Dr. Russell Eberhart in 1995 which was inspired by social behavior of bird flocking or fish schooling. In order to improve the performance of PSO algorithm, number of its variants has been proposed in literature. Few variants such as PSO Bound have been designed differently, whereas others use various methods to tune the random parameters. PSO-Time Varying Inertia Weight (PSO-TVIW), PSO Random Inertia Weight (PSO-RANDIW), and PSO-Time Varying Acceleration Coefficients (PSO-TVAC), APSO-VI, LGSCPSOA and many more are based on parameter tuning. On similar principle, the proposed approach improves the performance of PSO algorithm by adding new parameter henceforth called as "acceleration to particle" in its velocity equation. Efficiency of the proposed algorithm is checked against other existing PSO, and results obtained are very encouraging.
Year
DOI
Venue
2016
10.1007/978-3-319-41000-5_31
ADVANCES IN SWARM INTELLIGENCE, ICSI 2016, PT I
Keywords
Field
DocType
PSO,PSO-TVAC,Parameter tuning
Particle swarm optimization,Flocking (texture),Mathematical optimization,Computer science,Algorithm,Multi-swarm optimization,Optimization algorithm,Acceleration,Inertia,Random parameters,Particle acceleration
Conference
Volume
ISSN
Citations 
9712
0302-9743
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Shailesh Tiwari1146.95
K. K. Mishra2407.98
Nitin Singh331.11
N. R. Rawal400.34