A number of algorithms based on the evolutionary processing have been proposed forcommunication networks backbone such as Genetic Algorithm (GA). However, there has beenlittle work on the SWARM optimization algorithms such as Particle Swarm Optimization(PSO) for backbone topology design. In this paper, the performance of PSO on GA isdiscussed and a new algorithm as PSOGA is proposed for the net...

Because of computational drawbacks of conventional numerical methods in solving complex optimization problems, researchers may have to rely on meta-heuristic algorithms. Particle swarm optimization (PSO) is one of the most widely used algorithms due to its simplicity of implementation and fast convergence speed. Also, the cuckoo search algorithm is a recently developed meta-heuristic optimizati...

This paper studies the synchronization of SA and AV Node Oscillators using PSO optimized RBF-based controllers systems. High levels of control activities may excite unmodeled dynamics of a system. This matter changes the rules of controlling the system and achieving an acceptable control performance. In fact, the objective here is to reach a trade-off between tracking performance and parametric...

The paper is based on feed forward neural network (FFNN) optimization by particle swarm intelligence (PSI) used to provide initial weights and biases to train neural network. Once the weights and biases are found using Particle swarm optimization (PSO) with neural network used as training algorithm for specified epoch, the same are used to train the neural network for training and classificatio...

A self-evolving neural network is developed using a combination of PSO and JPSO algorithms to predict the pull-out capacity of suction caissons in clay. The algorithm is proposed with the aim of reducing the network complexity without compromising accuracy. A database consisting of experiments performed on suction caissons is used to construct and validate the network model. The performance com...

Although Particle Swarm Optimization (PSO) is used in variety of applications; it has limitations in the training phase. In this work, a new enhancement for PSO is proposed to overcome such limitations. The proposed PSO optimization consists of two stages. In the first stage, a Gaussian Maximum Likelihood (GML) is added to PSO to update the last 25% of swarm particles, while in the second stage...

The Particle Swarm Optimisation (PSO) algorithm has been established as a useful global optimisation algorithm for multi-dimensional search spaces. A practical example is its success in training feed-forward neural networks. Such successes, however, must be judged relative to the complexity of the search space. In this paper we show that the effectiveness of the PSO algorithm breaks down when e...

Particle swarm optimization (PSO) algorithm has come to be widely used as a problem solving method in engineering and computer science. This algorithm is one of recently several highly desirable attributes, including the fact that the basic algorithm is very easy to understand and implement. It is similar in some ways to evolutionary algorithms, but requires less computational bookkeeping and g...

A new hybrid PSO-EA-DEPSO algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. This algorithm is shown to outperform RNN predictors trained off-line by PSO, EA, and DEPSO as well as a linear predictor trained by th...

Navigation and obstacle avoidance are very important issues for the successful use of an autonomous mobile robot. To allow the robot to move between its current and final configurations without any collision within the surrounding environment, motion planning needs much treatment. Thus to generate collision free path it should have proper motion planning as well as obstacle avoidance scheme. Th...