Particle swarm optimization (PSO) has been proposed as an alternative to traditional evolutionary algorithms. Yet, more efficient strategies are still needed to control the trade-off between exploitation and exploration in the search process for solving complex tasks with high dimensional and multimodal objective functions. In this work, the authors propose a new PSO approach to overcome the se...

Web Mining is a challenging task that searches for Web access patterns, Web structures and the regularity and dynamics of the Web contents. It provides efficient Web Personalization, System Improvement, Site Modification, Business Intelligence and Usage Characterization. High-dimensional Web Log File clustering is a challenging task and requires an efficient clustering technique. The efficiency...

Data clustering is a recognized data analysis method in data mining whereas K-Means is the well known partitional clustering method, possessing pleasant features. We observed that, K-Means and other partitional clustering techniques suffer from several limitations such as initial cluster centre selection, preknowledge of number of clusters, dead unit problem, multiple cluster membership and pre...

Particle swarm optimization (PSO) is an artificial intelligence (AI) technique that can be used to find approximate solutions to extremely difficult or impossible numeric maximization and minimization problems. Particle swarm optimization is an optimization method. It is an optimization algorithm, which is based on swarm intelligence. Optimization problems are widely used in different fields of...

This paper presents several novel approaches of particle swarm optimization (PSO) algorithm with new particle velocity equations and three variants of inertia weight to solve the optimal control problem of a class of hybrid systems, which are motivated by the structure of manufacturing environments that integrate process and optimal control. In the proposed PSO algorithm, the particle velocitie...

Inspired by the migratory behavior in the nature, a novel particle swarm optimization algorithm based on particle migration (MPSO) is proposed in this work. In this new algorithm, the population is randomly partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization with time varying inertia weight and acceleration coefficients (LPSO-TVAC). At perio...

In this paper, synthesis of unequally spaced linear antenna arrays based on an inheritance learning particle swarm optimization (ILPSO) is presented. In order to improve the optimization efficiency of the PSO algorithm, we propose an inheritance learning strategy that can be applied to different topology of different PSO algorithms. In ILPSO algorithm, each cycle contains several PSO optimizati...

Many areas in power systems require solving one or more nonlinear optimization problems. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. The proposed method utilizes the Particle Swarm Optimization (PSO) algor...

Algorithms derived by mimicking the nature are extremely useful for solving many real world problems in different engineering disciplines. Particle swarm optimization (PSO) especially has been greatly acknowledged for its simplicity and efficiency in obtaining good solutions for complex problems. However, premature convergence of the standard PSO and many of its variants is a downside particula...

This research focuses on continuous dimensional affect recognition from bodily expressions using feature optimization and adaptive regression. Both static posture and dynamic motion bodily features are extracted in this research. A hybrid particle swarm optimization (PSO) algorithm is proposed for feature selection, which overcomes premature convergence and local optimum trap encountered by con...