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. / 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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/283597 |
| Date | 15 February 2012 |
| Creators | Urade, Hemlata S., Patel, Rahila |
| Contributors | Department Computer Science & Engineering, RCERT, RTMNU Chandrapur, Maharashtra, India, Department Computer Science & Engineering, RCERT, RTMNU Chandrapur, Maharashtra, India |
| Publisher | IJCSN |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Article, Technical Report |
| Relation | IJCSN-2012-1-1-4, http://ijcsn.org/IJCSN-2012/1-1/IJCSN-2012-1-1-4pdf |
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