Absfmct-This paper presents a new approach to economic load dispatch (ELD) problems with non-smwth objective functions using a particle swarm optimization (PSO). In practice, ELD problems have non-smooth objective functions with equality and inequality constraints that make it difficult to find the global optimum using any mathematical approaches. In this paper, a new PSO framework is suggested...

Introduction Finite Element (FE) model updating entails tuning the model so that it can better reflect the measured data from the physical structure being modeled [1]. One fundamental characteristic of an FE model is that it can never be a true reflection of the physical structure but will forever be an approximation. In other words, FE updating fundamentally implies that we are identifying a b...

Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in the performance of PSO. As far as our investigation is concerned, most of the relevant researches are based on computer simulations and few of them are based on theoretical approach. In ...

A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm op...

The paper extends our pervious work on solving multi-contingency transient stability constrained optimal power flow problems (MC-TSCOPF) with the approach of particles swarm optimization (PSO). A hybrid PSO method that incorporates with a new wavelet theory based mutation operation, intends to improve the searching strategies on previously used PSO methods, is proposed to solve MC-TSCOPF proble...

In this paper the technique of particle swarm optimization (PSO) is applied for fractal image compression (FIC). With the help of this evolutionary algorithm effort is made to reduce the search complexity of matching between range block and domain block. One of the image compression techniques in the spatial domain is Fractal Image Compression but the main drawback of FIC is that it involves mo...

Particle Swarm Optimization (PSO) is a swarm intelligence optimization method inspired from birds’ flocking or fish schooling. Many improved versions of PSO are reported in literature, including some by the authors. Original as well as improved versions of PSO have proven their applicability to various fields like science, engineering and industries. Economic dispatch (ED) problem is one of the...

In this paper, an improvement of the particle swarm optimization (PSO) algorithm is proposed. The aim of this algorithm is to iteratively resolve the cost problem of the Multivariable Generalized Predictive Control (MGPC) method under multiple constraints previously reduced. An ill-conditioned chemical process modelled by an uncertain Multi-Input & Multi-Output (MIMO) plant is controlled in ord...

In this paper, we propose a Particle Swarm Optimization (PSO) based solution to the Orienteering Problem. A major problem in many PSO applications is the premature convergence of some particles to local optimums. To overcome this problem in our application, such particles are re-initiated; consequently, the exploration capability of PSO is improved. Furthermore, by employing Reduced Variable Ne...

Particle Swarm Optimization (PSO) is a metaheuristic that is highly used to solve monoand multi-objective optimization problems. Two well-differentiated PSO versions have been defined – one that operates in a continuous solution space and one for binary spaces. In this paper, a new version of the Binary PSO algorithm is presented. This version improves its operation by a suitable positioning of...