R.E. Zich

A new hybrid genetical-swarm algorithm for electromagnetic optimization

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 selection but also based on cultural and social evolution. A detailed description of the algorithm and numerical comparison of the different techniques are presented for a typical electromagnetic optimization problem.

Neuro-fuzzy predictive model for PV energy production based on weather forecast

This paper introduces an evolutionary optimization algorithm as a tool for training an Artificial Neural Network used for production forecasting of solar energy PV plants. This optimized procedure essentially represent a bio-inspired heuristic search technique which is used to solve complex forecasting problems modeled on the concepts of biological neurons. Some simulation results are reported to highlight advantages and drawbacks of the proposed method in order to suitably apply this algorithm to a neuro-fuzzy system application in solar energy production. The weather forecast data related to the PV plants are supplied by the airport service close to the production site and relative data are pre-processed using Fuzzy Logic techniques.

Improved PSO algorithms for electromagnetic optimization

Some variations over the basic particle swarm algorithm are here proposed, aimed at a more efficient search over the solution space and exhibiting a negligible overhead in complexity and speed. The proposed algorithms are then applied to the test case of a microwave filter to show their superior capabilities with respect to the conventional algorithm.

Particle swarm optimization of microwave microstrip filters

Particle swarm optimization is here applied to the design of a microwave microstrip line filter. A friction factor is introduced to slow down particles and speed up convergence, as well as a sort of implicit restart. A comparison with genetic algorithms is also presented.

Genetical Swarm Optimization: a New Hybrid Evolutionary Algorithm for Electromagnetic Applications

In this paper a new effective optimization algorithm suitably developed for electromagnetic applications called genetical swarm optimization (GSO) will be 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 optimization (PSO) and genetic algorithms (GA). This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). The algorithm is tested here with respect to the other optimization techniques dealing with two typical problems, a purely mathematical one, the search for the global maximum of a multi-dimensional sine function and an electromagnetic application, the optimization of a linear array

A new algorithm for antenna optimization in aerospace applications: The sounding rocket test case

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 best configuration that assure the effectiveness of the overall rocket design. The explosion of Internet and Social Network diffusion in the last years is having a direct impact on different research fields as computer science, economy, sociology and biological science. SNO concept is an optimization algorithm inspired by the emulation of decision making process in social network environments. The experimental results of the rocket antenna design will be presented to prove the effectiveness of this new algorithm.

Novel Speed-Bump Design and Optimization for Energy Harvesting From Traffic

In recent years, the increase in computational capability and development of innovative multiphysic techniques has determined a growing interest toward modeling and optimization in engineering system design for green energy applications. In this field, advanced soft computing techniques can be applied by engineers to several problems and to be used in an optimization process to find out the best design and, thus, to improve the system performance. These techniques also promise to give new impulse to research on renewable systems and, particularly in the last five years, on the so-called energy-harvesting devices (EHDs). This paper presents the optimization of a tubular permanent-magnet linear generator used for applications of energy harvesting from traffic. The optimization process is developed by means of hybrid evolutionary algorithms to reach the best overall system efficiency and the impact on the environment and transportation systems. Finally, an experimental validation of the designed EHD prototype is presented.

Differentiated Meta-PSO for rectangular ring antenna with proximity-coupled feed

Particle Swarm Optimization (PSO), a robust stochastic evolutionary computational technique, has been here successfully applied to different electromagnetic optimization problems. In order to fulfill the requirement of fast convergence to get results in a very short time, a variation of original method has been proposed using the so called differentiated Meta-PSO. This paper presents the effectiveness of Meta-PSO technique in optimizing complex multidimensional problem relating to antenna engineering design implementation. To verify the robustness of this optimization tool and develop a full wave analysis, all these concepts are integrated into a case study represented by a rectangular ring antenna with proximity-coupled microstrip feed line.

Genetic optimization of microstrip reflectarrays

Printed reflectarrays try to combine the advantages of both reflectors and printed arrays (see e.g. [l]) and consist in arrays of printed patches, illuminated by a primary feed horn, that re-radiate the illuminating power back into the space. Since the elements have different positions, the field that propagates from the feed to the patches covers different path lengths and therefore the contributions to the total re-radiated field coming from the different patches are not in phase. So it becomes necessary to adjust the phase of the single element to compensate the different path lengths and this may be obtained using at least one of the following different possible techniques: adding to the patches suitable differentlength transmission lines [2, 31, varying the size of the radiating elements [4], rotating the elements to different angles or finally varying the dielectric constant of the material below the radiator itself. Here we chose to combine the first two options together

Optimization of a reflectarray antenna via hybrid evolutionary algorithms

In this paper a new effective optimization algorithm called genetical swarm optimization (GSO) will be presented. It has been 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 optimization (PSO) and genetic algorithms (GA). This algorithm is essentially a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). The algorithm is tested here with respect to the other optimization techniques dealing with the optimal design of an elliptical reflectarray antenna with printed elements and an off-set feed