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dc.contributor.authorUrade, Hemlata S.*
dc.contributor.authorPatel, Rahila*
dc.date.accessioned2013-04-23T00:05:56Z
dc.date.available2013-04-23T00:05:56Z
dc.date.issued2012-02-15
dc.identifier.issn2277–5420
dc.identifier.urihttp://hdl.handle.net/10150/283597
dc.descriptionOptimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Unconstrained optimization problems can be formulated as a D-dimensional minimization problem as follows: Min f (x) x=[x1+x2+……..xD] where D is the number of the parameters to be optimized. subjected to: Gi(x) <=0, i=1…q Hj(x) =0, j=q+1,……m Xε [Xmin, Xmax]D, q is the number of inequality constraints and m-q is the number of equality constraints. The particle swarm optimizer (PSO) is a relatively new technique. Particle swarm optimizer (PSO), introduced by Kennedy and Eberhart in 1995, [1] emulates flocking behavior of birds to solve the optimization problems.en_US
dc.description.abstractIn this paper the concept of dynamic particle swarm optimization is introduced. The dynamic PSO is different from the existing PSO’s and some local version of PSO in terms of swarm size and topology. Experiment conducted for benchmark functions of single objective optimization problem, which shows the better performance rather the basic PSO. The paper also contains the comparative analysis for Simple PSO and Dynamic PSO which shows the better result for dynamic PSO rather than simple PSO.
dc.language.isoenen_US
dc.publisherIJCSNen_US
dc.relation.ispartofseriesIJCSN-2012-1-1-4en_US
dc.relation.urlhttp://ijcsn.org/IJCSN-2012/1-1/IJCSN-2012-1-1-4pdfen_US
dc.subjectDynamic PSOen_US
dc.subjectMultiobjective Optimizationen_US
dc.titlePerformance Evaluation of Dynamic Particle Swarm Optimizationen_US
dc.typeArticleen_US
dc.typeTechnical Reporten_US
dc.contributor.departmentDepartment Computer Science & Engineering, RCERT, RTMNU Chandrapur, Maharashtra, Indiaen_US
dc.contributor.departmentDepartment Computer Science & Engineering, RCERT, RTMNU Chandrapur, Maharashtra, Indiaen_US
dc.identifier.journalInternational Journal of Computer Science and Networken_US
refterms.dateFOA2018-04-26T05:27:01Z
html.description.abstractIn this paper the concept of dynamic particle swarm optimization is introduced. The dynamic PSO is different from the existing PSO’s and some local version of PSO in terms of swarm size and topology. Experiment conducted for benchmark functions of single objective optimization problem, which shows the better performance rather the basic PSO. The paper also contains the comparative analysis for Simple PSO and Dynamic PSO which shows the better result for dynamic PSO rather than simple PSO.


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