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"In this paper a novel parallel meta-heuristic algorithm called MeTEO is presented, applied to the shape optimization of multistage depressed collectors, simulated by means of a Finite Element collector and electron gun simulator, COLLGUN, which uses the Constructive Solid Geometry for the description of the device shape. METEO is a hybrid algorithm composed by three different heuristics: FSO (Flock of Starlings Optimization), PSO (Particle Swarm Optimization), and BCA (Bacterial Chemotaxis Algorithm); it performs the optimization using both the topological and the metric rules and offers a natural parallel implementation that allows speeding up the whole process of optimization by the fitness modification (FM).

Purpose -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. Design/methodology/approach -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 þ BCA has been used to refine the FSO-found solutions, exploiting their better performances in local search. Findings -The optimization of the voltage of the electrodes of multistage depressed collector are efficiently handled with a moderate computational effort. Practical implication -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. Originality/value -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.

"Purpose – 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.. Design\/methodology\/approach – 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.. Findings – 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.. Practical implications – The paper describes the development of a novel efficient parallel method. for the solution of electromagnetic device optimization problems.. Originality\/value – 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 swarm based algorithms can be modelled, under suitable assumptions, as equivalent dynamic circuits reproducing the cinematic characteristics of the trajectories followed by the swarm members. This can be made in terms of voltages measured at the terminal of capacitors and currents measured at the terminals of inductors. Through the use of swarm-circuits the role played by the parameters becomes clear since it is possible to apply the stability analysis of continuous systems. This allows us to govern the exploration and/or the exploitation properties of the system simply by tuning its parameters into the convergence range or vice versa. The presented circuital model has been tested on famous benchmarks for optimization and inverse problems. The obtained results show that the swarm circuits are capable to manage exploration as well exploitation and can be used for real-time optimizations such as navigation in unknown ambient of mobile robots and so on