the vast use of linear prediction coefficients (lpc) in speech processing systems has intensified the importance of their accurate computation. this paper is concerned with computing lpc coefficients using evolutionary algorithms: genetic algorithm (ga), particle swarm optimization (pso), dif-ferential evolution (de) and particle swarm optimization with differentially perturbed velocity (pso-dv...

This paper introduces the Particle Swarm Optimization (PSO) algorithm to solve the optimal network reconfiguration problem for power loss reduction. The PSO is a relatively new and powerful intelligence evolution method for solving optimization problems. It is a population-based approach. The PSO was inspired from natural behavior of the bees on how they find the location of most flowers. The p...

The parts optimization are very important for scroll compressor design. According to existing problems of current optimization algorithm and actual optimization problems, the improved optimization algorithm—genetic-particle swarm optimization (GA-PSO) is proposed for scroll plate optimization. The optimization method integrates crossover of genetic algorithm (GA) and evolutionary mechanism of p...

In this paper, a hybrid particle swarm optimization algorithm (HPSOM) that uses the mutation process to improve the standard particle swarm optimization (PSO) algorithm is presented. The main idea of the HPSOM is to integrate the PSO with genetic algorithm mutation method. As a result, the proposed algorithm has the automatic balance ability between global and local searching abilities. The val...

Particle swarm optimization (PSO) has been employed on several optimization problems, including the clustering problem. PSO has also been employed in the clustering of data of different structure and dimensionality. In this paper it is employed in the clustering of nucleic acid sequences. The application of clustering, as a statistical tool, in the analysis of data of varied complexity has been...

Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal Optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide varie...

Otsu’s method is one of the best image thresholding techniques, but its complexity increases tremendously with the number of thresholds. In this work, Otsu’s criterion function is reduced into a suitable form for easy computation. An algorithm using Particle Swarm Optimization (PSO) is then proposed which works much faster than the original Otsu multi thresholding. This method achieves performa...

How to keep a balance between exploitation and exploration in Particle Swarm Optimization (PSO) for efficiently solving various optimization problems is an important issue. In order to handle premature convergence in PSO search, this paper proposes a novel algorithm, called Particle Swarm Optimization with Diversive Curiosity (PSO/DC), that introduces a mechanism of diversive curiosity into PSO...

FACTS devices plays a significant role to control the power flow of power transmission system. In this paper, a hybrid PSO algorithm is proposed to optimize the location of UPFC in power system. The proposed hybrid PSO algorithm has solved the formulated multiobjective optimization problem. This paper, five objective function to be considered in the form of minimization such as the fast voltage...

Particle swarm optimization (PSO) has been shown to perform well on many optimization problems. However, the PSO algorithm often can not find the global optimum, even for unimodal functions. It is necessary to study the local search ability of PSO. The interval compression method and the probabilistic characteristic of the searching interval of particles are used to analyze the local search abi...