School of Mathematics, Statistics, and Computer Science, University of Kwazulu-Natal, Private Bag X54001, Durban 4000, South Africa

Arasomwan, M.A., School of Mathematics, Statistics, and Computer Science, University of Kwazulu-Natal, Private Bag X54001, Durban 4000, South Africa; Adewumi, A.O., School of Mathematics, Statistics, and Computer Science, University of Kwazulu-Natal, Private Bag X54001, Durban 4000, South Africa

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. © 2013 Martins Akugbe Arasomwan and Aderemi Oluyinka Adewumi.

acceleration; algorithm; article; controlled study; global optimization; linear decreasing inertia weight; parameters; particle size; particle swarm optimization; problem solving; process optimization; quality control; simulation; stochastic model; task performance; velocity; animal; animal behavior; bird; physiology; theoretical model; Algorithms; Animals; Behavior, Animal; Birds; Models, Theoretical