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Performance Evaluation of Dynamic Particle Swarm Optimization
| dc.contributor.author | Urade, Hemlata S. | |
| dc.contributor.author | Patel, Rahila | |
| dc.date.accessioned | 2013-04-23T00:05:56Z | |
| dc.date.available | 2013-04-23T00:05:56Z | |
| dc.date.issued | 2012-02-15 | |
| dc.identifier.issn | 2277–5420 | |
| dc.identifier.uri | http://hdl.handle.net/10150/283597 | |
| dc.description | Optimization 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.abstract | In 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.iso | en | en_US |
| dc.publisher | IJCSN | en_US |
| dc.relation.ispartofseries | IJCSN-2012-1-1-4 | en_US |
| dc.relation.url | http://ijcsn.org/IJCSN-2012/1-1/IJCSN-2012-1-1-4pdf | en_US |
| dc.subject | Dynamic PSO | en_US |
| dc.subject | Multiobjective Optimization | en_US |
| dc.title | Performance Evaluation of Dynamic Particle Swarm Optimization | en_US |
| dc.type | Article | en_US |
| dc.type | Technical Report | en_US |
| dc.contributor.department | Department Computer Science & Engineering, RCERT, RTMNU Chandrapur, Maharashtra, India | en_US |
| dc.contributor.department | Department Computer Science & Engineering, RCERT, RTMNU Chandrapur, Maharashtra, India | en_US |
| dc.identifier.journal | International Journal of Computer Science and Network | en_US |
| refterms.dateFOA | 2018-04-26T05:27:01Z | |
| html.description.abstract | In 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. |
