Search results

The purpose of this paper is to present the application of a novel hybrid algorithm, called MeTEO (Metric‐Topological‐Evolutionary‐Optimization), based on the combination of three heuristics inspired by artificial life to the solution of optimization problems of a real electronic vacuum device.

The Particle Swarm Optimization (PSO), the Flock‐of‐Starlings Optimization (FSO) and the Bacterial Chemotaxis Algorithm (BCA) were adapted to implement a novel meta‐heuristic MeTEO the FSO has been powerfully employed for exploring the whole space of solutions, whereas the PSO is used to explore local regions where FSO had found solutions, and BCA to refine the solutions found by PSO, thanks its better performances in local search.

The optimization of the focusing magnetic field of a Travelling Wave Tubes (TWT) collector is presented in order to show the effectiveness of MeTEO, in combination with COLLGUN FE simulator and equivalent source representation. The optimization of the focusing magnetic structure is obtained by using a maximum of 100 steps for each heuristic.

The paper describes the development of a novel efficient parallel method for the solution of electromagnetic device optimization problems.

The paper shows the capabilities of a novel combination of optimization methods inspired by “artificial life” which allows us to achieve effective solutions of multimodal optimization problems, typical of the electromagnetic device optimization, with an acceptable computational cost, thanks also to its natural parallel implementation.

The paper's aim is to focus on the utilization of the GRID distributed computing environment in order to reduce simulation time for parameter studies of travelling wave tube (TWT) electron guns and helix slow‐wave structures.

Two TWT finite‐element analysis modules were adapted to be run on the GRID, for this purpose scripts were written to submit a collection of independent jobs (the parameter study) to the GRID and collect the results.

A 25‐job electron gun parameter study runs on the GRID in 30‐40 min instead of 7 h locally. A 16‐job slow‐wave structure parameter study runs in 1 h on the GRID instead of 8 h locally. Turnaround time on the GRID was limited by priority levels presently set by GRID management for the various jobs submitted.

The procedures guarantee a remarkable reduction of the computing time.

For heavy‐computational cost tasks such as the above finite element electromagnetic calculations, the effective use of a heterogeneous, distributed, computing platform (the GRID computing platform) is very advantageous. The paper shows the development of new generation collaborative tools.

This paper aims the application of a novel hybrid algorithm, called MeTEO, based on the combination of three heuristics inspired by artificial life to the optimization of electrodes voltages of multistage depressed collector.

The flock-of-starlings optimization (FSO), the particle swarm optimization (PSO) and the bacterial chemotaxis algorithm (BCA) were adapted to implement a hybrid and parallel algorithm: the FSO has been powerfully employed for exploring the whole space of solutions, whereas the PSO+BCA has been used to refine the FSO-found solutions, exploiting their better performances in local search.

The optimization of the voltage of the electrodes of multistage depressed collector are efficiently handled with a moderate computational effort.

The development of an efficient method for the solution of a complicated electromagnetic optimization problem, exploiting the different characteristic of different approaches based on evolutionary computation algorithm.

The paper shows that the combination of stochastic methods having different exploration properties with appositely developed FE electromagnetic simulator allows us to produce effective solutions of multimodal electromagnetic optimization problems, with an acceptable computational cost.