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This paper introduces an improved particle swarm optimization (PSO) as a new tool for training an artificial neural network (ANN). As a consequence, an accurate comparison with other optimization methods is needed; the typical supervised feed-forward backpropagation algorithm (EBP) and the classical genetic algorithm (GA) are chosen. The aim is to highlight(More)
—In this paper, a modified Bayesian Optimization Algorithm (BOA), named M-BOA, is proposed to introduce a suitable mutation scheme for the traditional procedure in order to speed up the convergence of the algorithm and to avoid it to be trapped in local minima or to stagnate in suboptimal solutions. The proposed algorithm has been applied both to a specific(More)
This paper aims to optimize the design of a novel antenna for aerospace applications to be integrated on an experimental rocket that has been designed in an advanced research student program. In order to optimize EM performance of such a system a novel optimization algorithm called SNO, Social Network Optimization, has been developed and tested to find the(More)
A new hybrid evolutionary algorithm called GSO (genetical swarm optimization) Is here presented. GSO combines the well known particle swarm optimization and genetic algorithms. The GSO algorithm is essentially a population-based heuristic search technique which can be used to solve combinatorial optimization problems, modeled on the concept of natural(More)
—In this paper a new class of hybridization strategies between GA and PSO is presented and validated. The Genetical Swarm Optimization (GSO) approach is presented here with respect with different test cases to prove its effectiveness. GSO is a hybrid evolutionary technique developed in order to exploit in the most effective way the uniqueness and(More)
AbSTrACT Since energy use is a type of consumer behavior reflecting the interests to maximize some objective function, the human being activities seen in energy terms might be used to create the social aggregations or groups. Electric energy generated from ecologic sources brings some unpredict-ability. Authors model the unpredictability of the distributed(More)
This paper introduces a hybrid evolutionary optimization algorithm as a tool for training an Artificial Neural Network used for production forecasting of solar energy PV plants. This hybrid technique is developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches, Particle Swarm(More)
Nowadays wireless sensor netwoks (WSN) technology, wireless communications and digital electronics have made it realistic to produce a large scale miniaturized devices integrating sensing, processing and communication capabilities. The focus of this paper is to present an innovative mobile platform for heterogeneous sensor networks, combined with adaptive(More)