Salvatore Coco, Antonino Laudani, Francesco Riganti Fulginei and Alessandro Salvini
The purpose of this paper is to apply a hybrid algorithm based on the combination of two heuristics inspired by artificial life to the solution of optimization problems.
Abstract
Purpose
The purpose of this paper is to apply a hybrid algorithm based on the combination of two heuristics inspired by artificial life to the solution of optimization problems.
Design/methodology/approach
The flock‐of‐starlings optimization (FSO) 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 BCA has been used to refine the FSO‐found solutions, thanks to its better performances in local search.
Findings
A good solution of the 8‐th parameters version of the TEAM problem 22 is obtained by using a maximum 200 FSO steps combined with 20 BCA steps. Tests on an analytical function are presented in order to compare FSO, PSO and FSO+BCA algorithms.
Practical implications
The development of an efficient method for the solution of optimization problems, exploiting the different characteristic of the two heuristic approaches.
Originality/value
The paper shows the combination and the interaction of stochastic methods having different exploration properties, which allows new algorithms able to produce effective solutions of multimodal optimization problems, with an acceptable computational cost, to be defined.
Details
Keywords
Salvatore Coco, Antonino Laudani, Giuseppe Pulcini, Francesco Riganti Fulginei and Alessandro Salvini
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…
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+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.
Details
Keywords
Salvatore Coco, Antonino Laudani, Giuseppe Pollicino, Giuseppe Pulcini, Francesco Riganti Fulginei and Alessandro Salvini
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…
Abstract
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.
Details
Keywords
Salvatore Coco, Antonino Laudani, Francesco Riganti Fulginei and Alessandro Salvini
This paper aims the application of a novel synergy between a neural network (NN) and the finite element method (FEM) in the solution of electromagnetic problem involving…
Abstract
Purpose
This paper aims the application of a novel synergy between a neural network (NN) and the finite element method (FEM) in the solution of electromagnetic problem involving hysteretic material in unbounded domain.
Design/methodology/approach
The hysteretic nature of the material is taken into account by an original NN able to perform the modelling of any kind of quasi-static loop (saturated and non-saturated, symmetric or asymmetric). An appositely developed iterative FEM procedure is presented for the solution of this kind of problems in unbounded domains.
Findings
By starting from a small set of measured loops, the NN manages the values of the magnetic field, H, and the flux density, B, as inputs while the differential permeability is the output. In particular, the proposed NN is capable to perform the modelling of saturated and non-saturated, symmetric or asymmetric hysteresis loops.
Practical implications
The development of an efficient method for the solution of a complicated electromagnetic problem in unbounded domain by using an iterative approach and NNs, which can be implemented also in existing FEM code.
Originality/value
The paper shows that the combination of FEM, iterative procedure and NNs allows us to produce effective solutions of electromagnetic problems in unbounded domains involving also nonlinear hysteretic magnetic materials with an acceptable computational cost.
Details
Keywords
Francesco Riganti Fulginei and Alessandro Salvini
The purpose of the present paper is to show a comparative analysis of classical and modern heuristics such as genetic algorithms, simulated annealing, particle swarm optimization…
Abstract
Purpose
The purpose of the present paper is to show a comparative analysis of classical and modern heuristics such as genetic algorithms, simulated annealing, particle swarm optimization and bacterial chemotaxis, when they are applied to electrical engineering problems.
Design/methodology/approach
Hybrid algorithms (HAs) obtained by a synergy between the previous listed heuristics, with the eventual addiction of the Tabu Search, have also been compared with the single heuristic performances.
Findings
Empirically, a different sensitivity for initial values has been observed by changing type of heuristics. The comparative analysis has then been performed for two kind of problems depending on the dimension of the solution space to be inspected. All the proposed comparative analyses are referred to two corresponding different cases: Preisach hysteresis model identification (high dimension solution space) and load‐flow optimization in power systems (low dimension solution space).
Originality/value
The originality of the paper is to verify the performances of classical, modern and hybrid heuristics for electrical engineering applications by varying the heuristic typology and by varying the typology of the optimization problem. An original procedure to design a HA is also presented.
Details
Keywords
Antonino Laudani, Salvatore Coco and Francesco Riganti Fulginei
The paper aims to illustrate the two kinds of analysis approach for which finite element method (FEM) can be successfully employed: the Poisson-Nernst-Planck (PNP) model and the…
Abstract
Purpose
The paper aims to illustrate the two kinds of analysis approach for which finite element method (FEM) can be successfully employed: the Poisson-Nernst-Planck (PNP) model and the Langevin-Lorentz-Poisson (LLP) one.
Design/methodology/approach
The approach of this work is to try making a survey of the use of the FEM in the modelling of charge transport/ion flow across membrane channels, in particular for the PNP analysis and for a particle based model such as LLP model.
Findings
In this paper, the two kinds of analysis approach for which FEM can be successfully employed, the PNP model and the LLP one, have been shown. In both cases the FEM is extremely useful to carry out these analysis and the simulation results obtained are in good agreement with experimental results.
Originality/value
The value of this paper is to demonstrate the FEM is extremely useful to carry out analysis and results which are in good agreement with experimental ones.