A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine...

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Bibliographic Details
Published in:PloS one 2016-08, Vol.11 (8), p.e0161558-e0161558
Main Authors: Amoshahy, Mohammad Javad, Shamsi, Mousa, Sedaaghi, Mohammad Hossein
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial Intelligence
Biology and Life Sciences
Computational Biology
Computer and Information Sciences
Computer Simulation
Electrical engineering
Evolutionary algorithms
Exploitation
Exploration
Inertia
International conferences
Models, Statistical
Optimization
Optimization algorithms
Optimization theory
Particle swarm optimization
Physical Sciences
Problems
Research and Analysis Methods
S parameters
Social Sciences
Software
Statistical analysis
Strategy
Swarm intelligence
Time Factors
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Summary:Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO's parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0161558