This paper presents two new strategies for navigation of a swarm of robots for target/mission focused applications including landmine detection and firefighting. The first method presents an embedded fuzzy logic approach in the particle swarm optimization (PSO) algorithm robots and the second method presents a swarm of fuzzy logic controllers, one on each robot. The framework of both strategies...

Grid computing is a computing framework to meet the growing computational demands. This paper introduces a novel approach based on Particle Swarm Optimization (PSO) for scheduling jobs on computational grids. The representations of the position and velocity of the particles in the conventional PSO is extended from the real vectors to fuzzy matrices. The proposed approach is to dynamically gener...

In this work, three control techniques of minimization torque ripple in SRM are proposed and compared to the classical technic names as Hysteresis [1]. The principle these methods is based on Torque Sharing Function (TSF) fixing reference each phase. literature review, method used reduction ripples TSF-Hysteresis Controller (TSF-H). first TSF-Predictive (TSF-P), here objective find voltage be a...

Chaotic catfish particle swarm optimization (C-CatfishPSO) is a novel optimization algorithm proposed in this paper. C-CatfishPSO introduces chaotic maps into catfish particle swarm optimization (CatfishPSO), which increase the search capability of CatfishPSO via the chaos approach. Simple CatfishPSO relies on the incorporation of catfish particles into particle swarm optimization (PSO). The in...

This study proposes an efficient non-parametric classifier for bankruptcy prediction using an adaptive fuzzy k -nearest neighbor (FKNN) method, where the nearest neighbor k and the fuzzy strength parameter m are adaptively specified by the particle swarm optimization (PSO) approach. In addition to performing the parameter optimization for FKNN, PSO is utilized to choose the most discriminative ...

In order to improve the performance of PSO algorithm, number of its variants has been made. This paper presents detail overview of the basic concepts of PSO and its variants. Many variants of PSO have been developed due to improved speed of convergence and quality of solution found by Researchers. The Applications of PSO in Complex Environments is discussed. Modifications, both those already de...

In this paper, an automatic classifier has been developed using Feed Forward Neural Network (FFNN) to classify the ECG signals between different heartbeats. Here, the classifier is trained independently bymorphological, heartbeat interval features and temporal features using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The trained classifier then classifies the be...

Inertia weight is one of the most important adjustable parameters of particle swarm optimization (PSO). The proper selection of inertia weight can prove a right balance between global search and local search. In this paper, a novel PSOs with non-linear inertia weight based on the arc tangent function is provided. The performance of the proposed PSO models are compared with standard PSO with lin...