Open AccessAn enhanced particle swarm optimization algorithm
Author(s) -
Wameedh Riyadh AbdulAdheem
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i6.pp4904-4907
Subject(s) - particle swarm optimization , benchmark (surveying) , convergence (economics) , inertia , computer science , mathematical optimization , algorithm , exponential function , swarm behaviour , construct (python library) , mathematics , physics , mathematical analysis , geodesy , classical mechanics , geography , economics , programming language , economic growth
In this paper, an enhanced stochastic optimization algorithm based on the basic Particle Swarm Optimization (PSO) algorithm is proposed. The basic PSO algorithm is built on the activities of the social feeding of some animals. Its parameters may influence the solution considerably. Moreover, it has a couple of weaknesses, for example, convergence speed and premature convergence. As a way out of the shortcomings of the basic PSO, several enhanced methods for updating the velocity such as Exponential Decay Inertia Weight (EDIW) are proposed in this work to construct an Enhanced PSO (EPSO) algorithm. The suggested algorithm is numerically simulated established on five benchmark functions with regards to the basic PSO approaches. The performance of the EPSO algorithm is analyzed and discussed based on the test results.