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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.
This paper presents a recently developed algorithm based on the emulation of decision making process in social network environments, called Social Network Optimization (SNO). The design of a sparse array is here addressed in order to assess SNO's performance on a benchmark EM optimization problem. Reported results show its effectiveness in dealing with EM problems.
This paper presents a novel version of PSO, called Black-hole PSO (bhPSO),which is here considered for antennas optimization. It is based on the concept of repulsion among particles in case of stagnation. The design of planar arrays and reflectarrays is addressed in order to assess its performances on EM optimization problem. Reported results show its effectiveness in dealing with multi-dimensional...
In recent years there has been an increasing attention to novel evolutionary optimization techniques. A recently developed algorithm called Social Network Optimization (SNO), based on the emulation of decision making process in social network environments, is here considered and compared to Stud Genetic Algorithm (SGA). The design of a planar array is here addressed in order to compare their performances...
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