in this paper, we propose a novel algorithm to enhance the noisy speech in the framework of dual-channel speech enhancement. the new method is a hybrid optimization algorithm, which employs the  combination of  the  conventional θ-pso and the shuffled sub-swarms particle optimization (sspso) technique. it is known that the θ-pso algorithm has better optimization performance than standard pso al...

Particle swarm optimization (PSO) is a population based statistical optimization technique which is inspired by social behavior of bird flocking or fish schooling. The main weakness of PSO especially in multimodal problems is trapping in local minima. Recently a learning automata based PSO called PSO-LA to improve the performance of PSO has been reported. PSO-LA uses one learning automaton for ...

The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection optimum process parameter levels in any process, numerous metaheuristic algorithms have been proposed so far. However, many are either computationally too expensive or become trapped pit local optima. counter these challenges, this paper, hybrid called PSO-GSA emp...

In this paper a new evolutionary learning algorithm based on a hybrid of improved real-code genetic algorithm (IGA) and particle swarm optimization (PSO) called HIGAPSO is proposed to solve the optimal reactive power dispatch (ORPD) Problem. In order to overcome the drawbacks of standard genetic algorithm and particle swarm optimization, some improved mechanisms based on non-linear ranking sele...

one of the most important fuel cells (fcs) is proton exchange membrane fuel cells (pemfcs). the outputvoltage of this fc depends on current loads. this paper tries to introduce, implement and control the voltage ofpemfc, during load variations. the output voltage of fuel cell should be constant during load variation. toachieve this goal, a controller should be designed. here, the lead-lag contr...

Since chaos systems generally have the intrinsic properties of sensitivity to initial conditions, topological mixing and density of periodic orbits, they may tactfully use the chaotic ergodic orbits to achieve the global optimum or their better approximation to given cost functions with high probability. During the past decade, they have increasingly received much attention from academic commun...

Our main objective in this article is to achieve minimum side lobe levels for a specific first null beam-width and also a minimum size of the circumference by an optimization-based design method for non-uniform, planar, and circular antenna arrays. Our approach is based on a new variant of Particle swarm Optimization technique. This new technique is a hybrid of Local Neighborhood based PSO with...

Applications of the Particle Swarm Optimization (PSO) to solve image processing problem with a reference to a new automatic enhancement technique based on real-coded particle swarms is proposed in this paper. The enhancement process is a non-linear optimization problem with several constraints. The objective of the proposed PSO is to maximize an objective fitness criterion in order to enhance t...

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...