NDLI: On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

Content Provider	PubMed Central
Author	Arasomwan, Martins Akugbe Adewumi, Aderemi Oluyinka
Copyright Year	2013
Abstract	Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.
Related Links	http://dx.doi.org/10.1155/2013/860289
Starting Page	860289
File Format	PDF
ISSN	1537744X
e-ISSN	1537744X
Journal	The Scientific World Journal
Volume Number	2013
Language	English
Publisher	Hindawi Publishing Corporation
Publisher Date	2013-10-31
Access Restriction	Open
Rights Holder	Hindawi Publishing Corporation
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Medicine Biochemistry, Genetics and Molecular Biology Environmental Science

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