University of Bahrain
Scientific Journals

B-Positive Particle Swarm Optimization (B.P.S.O)

Show simple item record Tabassum*, Muhammad Naveed Meer, Rashed Ali, Zeshan 2018-07-31T08:49:33Z 2018-07-31T08:49:33Z 2013
dc.identifier.issn 2210-1519
dc.description.abstract This paper introduces a comparatively new technique for Particle Swarm Optimization (P.S.O). The standard P.S.O technique is modified in a unique way to come up with B-positive Particle Swarm Optimization. The B.P.S.O which is simulated in Matlab lets the particles in a multi-dimensional space to move in an overall positive direction. In other words the particles are made to move from one side of the space to the other without negative (backward) displacement in search of the global best position. At the same time the displacement magnitude is slightly reduced randomly to discourage the particles from jumping out of the space boundary. The lost particles are randomly thrown around the then known best position, this in return saves a lot of time and effort resulting in improved overall simulation results. Five popular benchmark functions are used to evaluate the performance of B.P.S.O and the result in terms of mean and standard deviation values for global minimum and mean time per replica are compared with previous Standard P.S.O results. The B.P.S.O turns out to be more efficient in terms of optimum convergence and simulation speed. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International *
dc.rights.uri *
dc.subject Particle swarm optimazation
dc.subject swarm intelligence
dc.subject artificial intelligence
dc.title B-Positive Particle Swarm Optimization (B.P.S.O) en_US
dc.type Article en_US
dc.volume 01
dc.issue 02
dc.source.title International Journal of Computing and Network Technology
dc.abbreviatedsourcetitle IJCNT

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