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The aim of this article is to show how the behavior of Social Network Optimization (SNO) changes with a change of the population size. Then two different approaches will be shown: the variable population and the adaptive population. At the end this last approach will be tested on an antenna optimization.
In the last decades several population-based heuristic search techniques have been developed for the optimization of combinatorial electromagnetic problems, all of them are modeled on the concepts of natural selection and evolution e.g. Genetic Algorithm (GA), Particle Swarm Optimization (PSO). Moreover, vast variations of original methods can be found in the literature. In this paper, with the intent...
In this paper, a new approach for sparse array synthesis is proposed. It is based on the use of a recently introduced improved version of the Bayesian Optimization Algorithm (BOA), named Modified BOA (M-BOA), that has already proved its outperforming capabilities with respect to the standard BOA as well as other well-know optimization approaches. Moreover, in opposite to what is generally done relatively...
Nowadays the design of complex real electrical, electronic or electromagnetic systems may effectively exploit the characteristics of population based global optimizers. One of the main drawbacks of the adoption of these optimizers in the design of a real system is the difficulty in the introduction, in the optimized design algorithm, of all the heuristic knowledge already available in the field. In...
Design of electrical and electronic systems with complex EMC constrains requires often to exploit the peculiarities of some population based global optimizers. One of the main drawbacks of the adoption of these optimizers for system design is represented by the difficulty of introducing in the algorithm all the heuristic knowledge already available in the field. In order to overcome this problem,...
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