Fabio Alessandro Guerra

A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization

Numerous optimization methods have been proposed for the solution of the unconstrained optimization problems, such as mathematical programming methods, stochastic global optimization approaches, and metaheuristics. In this paper, a metaheuristic algorithm called Modified Shuffled Complex Evolution (MSCE) is proposed, where an adaptation of the Downhill Simplex search strategy combined with the differential evolution method is proposed. The efficiency of the new method is analyzed in terms of the mean performance and computational time, in comparison with the genetic algorithm using floating-point representation (GAF) and the classical shuffled complex evolution (SCE-UA) algorithm using six benchmark optimization functions. Simulation results and the comparisons with SCE-UA and GAF indicate that the MSCE improves the search performance on the five benchmark functions of six tested functions.

B-spline neural network design using improved differential evolution for identification of an experimental nonlinear process

B-Spline Neural Network (BSNN), a type of basis function neural network, is trained by gradient-based methods which may fall into local minima during the learning procedure. To overcome the limitations encountered by gradient-based optimization methods, we propose differential evolution (DE) - an evolutionary computation methodology - which can provide a stochastic search to adjust the control points of a BSNN. In this paper, we propose six DE approaches using chaotic sequences based on logistic mapping to train a BSNN. Chaos describes the complex behavior of a nonlinear deterministic system. The application of chaotic sequences instead of random sequences in DE is a powerful strategy to diversify the DE population and improve the DEs performance in preventing premature convergence to local minima. The numerical results presented here indicate that chaotic DE was effective for building a good BSNN model for the nonlinear identification of an experimental nonlinear yo-yo motion control system.

Multivariable nonlinear boiler power plant identification through neural networks and Particle Swarm Optimization approaches

The identification of nonlinear systems with artificial neural networks models has been successfully used in many applications. Most processes in industry are characterized by nonlinear and time-varying behavior. In this context, the identification of mathematical models for nonlinear systems is vital in many fields of engineering. The Radial Basis Function Neural Network (RBF-NN) is a powerful approach for nonlinear identification and can be improved using Particle Swarm Optimization (PSO) approaches. This paper presents a multivariable nonlinear system identification using RBF-NN combined with standard PSO and Constriction Factor PSO (CFPSO) approaches in order to determine the RBF-NN parameters. RBF-NN is considered to be a good choice for black-box modeling problems due to its rapid learning capacity and, therefore, has been applied successfully to nonlinear time series modeling and nonlinear identification. On the other hand, PSO was inspired by the choreography of bird flocks and fish schools and can be seen as a distributed behavior algorithm that performs multidimensional search. Furthermore, promising simulation results from performance analysis of the proposed RBF-NN with PSO training approaches are presented and discussed in this paper showing promising results.

Electrical Transmission Lines Design through Integer Multiobjective Particle Swarm Optimization Approach

Electrical Transmission Lines (ETL) design is normally performed aiming to reach minimum cost while satisfying project requirements. However, achieving one only design goal may not be sufficient, as the designer may need to impose other technical design metrics. This work aims to perform the ETL design task while offering the designer the possibility to impose other design goals than cost, like the ETL reliability efficiency. Multiobjective optimization algorithms are therefore suggested to perform the design task. An improved multiobjective integer version of Particle Swarm Optimization, the Integer Multiobjective Particle Swarm Optimization (IMOPSO), is proposed in this paper. The IMOPSO is applied in order to solve the multiobjective ETL design showing promising results. Conclusions are drawn regarding the IMOPSO algorithm performance and design results, through statistical analysis.

Identificação do Sistema Dinâmico Caótico de Chua Usando uma Rede Neural Polinomial

In this paper, the Group Method of Data Handling(GMDH)-type neural network for chaotic dynamic systems identification is proposed. The GMDH algorithm can be considered as a structural identification technique or a feedforward polynomial neural network with a growing structure during the training process. This polynomial neural network uses Adalines with nonlinear preprocessors. The training of this neural network is realized by learning rule application of Widrow-Hoff. The neural network implementation is evaluate for identification of Chua chaotic system. An experimental study of nonlinear identification of Chua system shows successful results for the proposed approach.

A Differential Beta Quantum-behaved Particle Swarm Optimization for Circular Antenna Array Design

The classical particle swarm optimization (PSO) algorithm is inspired on biological behaviors such as the social behavior of bird flocking and fish schooling. In this context, many significant improvements related the updating formulas and new operators have been proposed to improve the performance of the PSO algorithm in the literature. On the other hand, recently, as an alternative to the classical PSO, a quantumbehaved particle swarm optimization (QPSO) algorithm was proposed. The contribution of this paper is linked with a modified QPSO based on beta probability distribution and mutation differential operator. The effectiveness of the proposed modified QPSO algorithm is demonstrated by solving three kinds of optimization problems including two benchmark functions and a circular antenna design problem.

Artificial immune network approach with beta differential operator applied to optimization of heat exchangers

The artificial immune systems combine these strengths have been gaining significant attention due to its powerful adaptive learning and memory capabilities. A meta-heuristic approach called opt-aiNET (artificial immune network for optimization) algorithm, a well-known immune inspired algorithm for function optimization, is adopted in this paper. The opt-aiNET algorithm evolves a population, which consists of a network of antibodies (considered as candidate solutions to the function being optimized). These undergo a process of evaluation against the objective function, clonal expansion, mutation, selection and interaction between themselves. In this paper, a proposed modified opt-aiNET approach using based on mutation operator inspired in differential evolution and beta probability distribution (opt-BDaiNET) is described and validated to three benchmark functions and to shell and tube heat exchanger optimization based on the minimization from economic view point. Simulations are conducted to verify the efficiency of proposed opt-BDaiNET algorithm and the results obtained for two case studies are compared with those obtained by using genetic algorithm and particle swarm optimization. In this application domain, the opt-aiNET and opt-BDaiNET were found to outperform the previously best-known solutions available in the recent literature.