Particle Swarm Optimisers are inherently distributed algorithms where the solution for a problem emerges from the interactions between many simple individual agents called particles. This article proposes the use of the Particle Swarm Optimiser as a new tool for Data Mining. In the first phase of our research, three different Particle Swarm Data Mining Algorithms were implemented and tested aga...

for a uniform distribution of water, decrease in water waste and decrease in erosion of soil, it is important that a land be prepared with proper slopes along its length as well as width. the aim of leveling is to create appropriate slopes for irrigation and drainage on the lands that were not already properly levelled and of the same time creating the level surface with a minimum transport of ...

In this paper, we introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the signilicant and effective impact of this new parameter on the particle swarm optimizer.

This paper introduces an effectual technique to solve the DNA sequence assembly problem using a variance of the standard Particle Swarm Optimization (PSO) called the Constriction factor Particle Swarm Optimization (CPSO).The problem of sequence assembly is one of the primary problems in computational molecular biology that requires optimization methodologies to rebuild the original DNA sequence...

Recently, the scheduling problem in distributed data-intensive computing environments has been an active research topic. This Chapter models the scheduling problem for work-flow applications in distributed dataintensive computing environments (FDSP) and makes an attempt to formulate the problem. Several meta-heuristics inspired from particle swarm optimization algorithm are proposed to formulat...

Particle swarm optimization (PSO) is a kind of evolutionary algorithm to find optimal solutions for continuous optimization problems. Updating kinetic equations for particle swarm optimization algorithm are improved to solve traveling salesman problem (TSP) based on problem characteristics and discrete variable. Those strategies which are named heuristic factor, reversion mutant and adaptive no...

In this paper, an effective combination of two Metaheuristic algorithms, namely Invasive Weed Optimization and the Particle Swarm Optimization, has been proposed. This hybridization called as HIWOPSO, consists of two main phases of Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO). Invasive weed optimization is the natureinspired algorithm which is inspired by colonial beha...

In order to improve performance of particle swarm optimization algorithm (PSO) in global optimization, the reason of premature convergence of the PSO is analyzed, and a new particle swarm optimization based on two subswarms (TSS-PSO) is proposed in this paper. The particle swarm is divided into two identical sub-swarms, that is, the first sub-swarm adopts basic PSO model to evolve, whereas the ...