Research Papers

Applying velocity initialization strategies in ALC-PSO

This paper presents the impact of applying various velocity initialization strategies on the ALC-PSO Algorithm.

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Stagnation Removal in PSO Algorithm

This paper presents the stagnation problem occurring in the PSO algorithm and some ways to remove this problem. A variant of PSO, called the PSO with Aging Leader with Challengers(ALC-PSO) overcomes this stagnation problem and improves the efficiency of the swarm.

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A Review of Issues in Self Organizing Networks

This paper presents the basic architecture of Self Organizing Networks( SON), some of the design issues related to SON architectures.

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Parameter Settings for implementing the Particle Swarm Optimization with Aging Leader and Challengers (ALC-PSO)

This paper presents the variant of PSO that has been developed to overcome the limitation of premature convergence and improve the performance of the algorithm.

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A Review of Issues in Image Segmentation using Particle Swarm Optimization

This paper presents some issues involved in the process of image segmentation using Particle Swarm Optimization

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PSO (Particle Swarm Optimization) Variants

This paper presents the various available variants of PSO , which enhances the performance of original PSO and improves the limitations which are there in original PSO algorithms such as: the solution gets stuck in local optima, premature convergence.

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A Review of Benchmark Functions used in Optimization Algorithms

This paper presents some of the benchmarks used in testing the validating the optimization algorithms.

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Benchmarks used in ALC-PSO

This paper presents the benchmark functions that have  been probably the most commonly adopted to assess performance of ALC-PSO-based algorithms and information on all of them are given, like the search range, the position of their known optima, and other relevant properties.

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Parameters determining Optimal Solution of Particle Swarm Optimization(PSO) algorithm – Role of Population size and number of
Iterations

This paper presents the simulation results of particle swarm optimization algorithm with varying the number of particles and number of iterations.

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