Carlos Eduardo Klein

Modified Social-Spider Optimization Algorithm Applied to Electromagnetic Optimization

Social-spider optimization (SSO) is a new nature-inspired algorithm of the swarm intelligence field to global optimization applications, which is based on the simulation of cooperative behavior of social spiders. To enhance the performance of the standard SSO, a modified SSO (MSSO) approach based on beta distribution and natural gradient local search was proposed in this paper. In order to verify the performance of the MSSO, tests using Loneys solenoid benchmark and a brushless direct current motor benchmark are realized comparing the effectiveness of the SSO and the proposed MSSO. The results of this paper demonstrated that the MSSO performance is promising in electromagnetics optimization.

Wavenet using artificial bee colony applied to modeling of truck engine powertrain components

The purpose of this paper is to validate an artificial wavelet neural network, or wavenet model, combined with artificial bee colony optimization, a swarm intelligence paradigm, to model powertrain components of a truck engine. The arrangement of artificial neural networks with wavelet based functions, called artificial wavelet neural network or wavenet (AWNN), creates a valuable tool to represent the nonlinear multivariable systems. AWNN can be considered a particular case of the feed-forward basis function neural network model. To illustrate the use of the proposed AWNN based on ABC optimization for the black-box modeling, we apply it to model a truck engine with a cubic displacement greater than seven liters. Identification results were carried out using AWNN implemented in Matlab computational environment and the model accuracy is evaluated based on performance indices. Final results were compared with Elman network, Jordan network and kernel adaptive filtering in order to check the AWNN performance. The comparison methods were tuned with the same optimization algorithm in order to find their best tuning parameters. The simulation results show that the artificial wavelet neural network approach can be useful and a promising technique in powertrain components modeling. This proposed AWNN combined with artificial bee colony approach allows modeling the dynamical behavior of powertrain components of a truck engine. Simulation techniques are becoming more present in automotive development process.This paper proposed an AWNN approach combined with an artificial bee colony.A good model of the system dynamics can facilitate model-based design.

Forecasting electricity prices using a RBF neural network With GARCH errors

In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose a novel estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide hour-ahead point and direction-of-change forecasts of the Spanish electricity pool prices.

A population-based simulated annealing algorithm for global optimization

Simulated annealing (SA) is a solo-search algorithm, trying to simulate the cooling process of molten metals through annealing to find the optimum solution in an optimization problem. SA selects a feasible starting solution, produces a new solution at the vicinity of it, and makes a decision by some rules to move to the new solution or not. However, the results found by SA depend on the selection of the starting point and the decisions SA makes. In this paper, in order to ameliorate the drawbacks of the algorithm, a population-based simulated annealing (PSA) algorithm is proposed. PSA uses the populations ability to seek different parts of the search space, thus hedging against bad luck in the initial solution or the decisions. A set of benchmark functions was used in order to evaluate the performance of PSA algorithm. Simulation results accentuate the superior capability of PSA in comparison with the other optimization algorithms.

Multi-objective symbiotic search algorithm approaches for electromagnetic optimization

Optimization metaheuristics are a powerful way to deal with many electromagnetic optimization problems. Recently, the symbiotic organisms search (SOS) algorithm was proposed to solve single-objective optimization problems. SOS mimics the symbiotic relationship among the living beings. In order to extend the classical mono-objective SOS algorithm approach, this paper proposes a new multi-objective SOS (MOSOS) and an improved IMOSOS. Results on a multi-objective constrained brushless direct current (DC) motor design show that the MOSOS and IMOSOS present promising performance.

A normative differential evolution approach for estimation of heat transfer coefficient during freezing treatment by inverse analysis

Among the existing meta-heuristic optimization algorithms, a well-known branch is the differential evolution (DE). DE is a powerful population-based algorithm of evolutionary computation field designed for solving global optimization problems which only has a few control parameters. With an eye to improve the performance of DE, in this paper, a DE approach combined with a cultural algorithm technique based on normative knowledge (NDE) is investigated to estimate the heat transfer coefficient during freezing treatment by inverse analysis. Numerical results for inverse heat transfer problem demonstrate the applicability and efficiency of the NDE algorithm. In this application, NDE approach outperforms a classical DE approach in terms of quality of solution.

Multiobjective Symbiotic Search Algorithm Approaches for Electromagnetic Optimization

Optimization metaheuristics is a powerful way to deal with many electromagnetic optimization problems. Their main advantages are that they don’t require gradient computation, they are more likely to give a global optimum solution and have a higher degree of exploration and exploitation ability. Recently, the symbiotic organisms search (SOS) algorithm was proposed to solve single-objective optimization problems. SOS mimics the symbiotic relationship among the living beings. In order to extend the classical mono-objective SOS algorithm approach, this paper proposes a new multiobjective SOS (MOSOS) based on nondominance and crowding distance criterion. Furthermore, an improved MOSOS (IMOSOS) based on normal (Gaussian) probability distribution function also was proposed and evaluated. Results on a multiobjective constrained brushless direct current (dc) motor design show that the MOSOS and IMOSOS present promising performance.

Firefly approach optimized wavenets applied to multivariable identification of a thermal process

The combination of wavelet theory and feedforward artificial neural networks has resulted in wavelet neural networks or wavenets (WNNs). In these networks, the activation functions are described by discrete wavelet functions. Due to the promising properties of time-frequency localization and multi-resolution signal processing of the wavelet transform combined with the approximation capability of artificial neural networks, WNNs have found applications in dynamic system identification field during the past years. The paper aims at the development of the WNN based on traditional firefly algorithm (FA). The proposed FA is based on Tinkerbell map to tune the spread of wavelets and number of selected wavelet bases. The FA is a stochastic metaheuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. In FA, the flashing light can be formulated in such a way that it is associated with the objective function to be optimized, which makes it possible to formulate the firefly algorithm. The efficacy of WNN with FA tuning is tested on the identification of a multivariable thermal process.

Enhanced Flower Pollination Approach Applied to Electromagnetic Optimization

It is difficult to use the deterministic mathematical tools such as a gradient method to solve global optimization problems. Flower pollination algorithm (FPA) is a new nature-inspired algorithm of the swarm intelligence field to global optimization applications, based on the characteristics of flowering plants. To enhance the performance of the standard FPA, an enhanced FPA (EFPA) approach based on beta probability distribution was proposed in this paper. In order to verify the performance of the proposed EFPA, five benchmark functions are chosen from the literature as the test suit. Furthermore, tests using Loney’s solenoid benchmark, a classical problem in the electromagnetics area, are realized to evaluate the effectiveness of the FPA and the proposed EFPA. Simulation results and comparisons with the FPA demonstrated that the performance of the EFPA approach is promising in electromagnetics optimization.

Power Demand Forecast Using Least-Squares Support Vector Machines

This paper aims to share the results on forecasting power demand using least-squares support vector machines. The development is based on model estimation taking in consideration the past measurements for power demand and ambient temperature. All approximated models were evaluated using the multiple correlation coefficient (R 2 ) or mean absolute percentage error (MAPE) and maximum error combined as quality parameters.