Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms ...

Back propagation algorithm (BPA) have the complexity, local minima problem so we are using Particle Swarm optimization (PSO) algorithms to reduce and optimize BPA. In this paper, two variants of Particle Swarm Optimization (PSO) PSO_Hill and PSO_A* is used as optimization algorithm. PSO_Hill and PSO_A* algorithms are analyzed and evaluated on the basis of their advantages, applied to feed forwa...

The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of...

the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...

in this paper, we have proposed a new algorithm which combines pso and ga in such a way that the new algorithm is more effective and efficient.the particle swarm optimization (pso) algorithm has shown rapid convergence during the initial stages of a global search but around global optimum, the search process will become very slow. on the other hand, genetic algorithm is very sensitive to the in...

This paper presents a Particle Swarm Optimization with Improved Inertia Wight (PSO-IIW) for Combined Heat and Power Economic Dispatch (CHPED) problem. The proposed PSO-IIW technique, which is a population based global search and optimization technique, has been developed to solve the CHPED problem. The CHPED problem is formulated as an optimization problem which is solved by the PSO-IIW techniq...

In order to improve the prediction ability of the Neuron Network in stock index prediction, we proposed an improve particle swarm neural network algorithm. The Particle Swarm Optimization algorithm based on Parasitic Immune (PSOPI) was used to optimize combination weights of BP neural network model parameters. The BP algorithm was also to obtain the parameters of network further accurate. Final...

in this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. scheduling algorithms play an important role in grid computing, parallel tasks scheduling and sending them to appr...

Evrimsel Algoritmalar (EA’lar), ağ tasarımı problemleri, yol bulma sosyal ve ekonomik planlama gibi karmaşık optimizasyon problemlerini çözmek için yaygın olarak kullanılan algoritmalardır. Kullandıkları akıllı yöntemler sayesinde EA’lar, yeni çözümler üretmek ilk çözümü, yinelemeli olarak, iyileştirirler. Sıklıkla EA'lardan biri Parçacık Sürü Optimizasyonu (PSO) algoritmasıdır. PSO algoritması...