Optimization of multistage depressed collectors using fem and parallel algorithm MeTEO

Abstract

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.

On the Generalization Capabilities of the Ten-Parameter Jiles-Atherton Model

“…The choice of the exploitation algorithm can go from simple gradient descent based techniques [47] to complex metaheuristics like the Bacterial Chemotaxis Algorithm often used in electromagnetic inverse problems [44]. Hybrid strategies are well versed to be implemented on parallel architecture, distributing the tasks of exploration and exploitation on the master and the slaves [18,49]. In this work, the identification problem is solved through a novel algorithm that features both exploration and exploitation capabilities and runs natively on parallel architecture.…”

“…strategy, similar to the one seen in [18,49,52], that features both exploration and exploitation capabilities. This strategy is thought to run on parallel architecture by the algorithm called CFSO 3 [53] depicted in Figure 2.…”

“…The identification process of the model is numerical and is approached through solving a least squares (LSQ) problem against some reference data that can be either experimental or simulated [35,36]. The use of this model for practical applications has been favored by the development of new optimization techniques, which allowed a finer identification of the model parameters [16,[37][38][39][40][41][42][43][44][45][46][47][48][49]. On the other hand, the core of the model was widely revised in the years.…”

Time-Domain Circuit Modelling for Hybrid Supercapacitors

“…Model identification is performed on data acquired by using a real Hybrid SC device through an experimental workbench. Identification of the model is performed with a hybrid two-step optimization algorithm suitable for non-linear problems, which exhibit strong protection against local minima entrapment, as shown in [9,10]. The generalization capability of the models is obtained by measuring the error against an independent dataset which has not been used during the identification process.…”