Francesco Grimaccia

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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)
Abstract: 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(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)
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)
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)
In the past years Particle Swarm Optimization (PSO) has gained increasing attention for engineering and realworld applications. Among these, the design of antennas and electromagnetic devices is a well-established field of application. More recently, Social Network Optimization (SNO) has been introduced, inspired by the recent explosion of social networks(More)
Socio-economics aims to understand the interplay between the society, economy, markets, institutions, self-interest, and moral commitments. It is a multidisciplinary field using methods from economics, psychology, sociology, history, and even anthropology. Socio-economics of networks have been studied for over 30 years, but mostly in the context of social(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 effective optimization algorithm called Genetical Swarm Optimization (GSO) is presented. This is an hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the Particle Swarm(More)
The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied(More)