IEEE student Member PhD student, REGIM:REsearch Group on Intelligent Machines University of Sfax, National School of Engineers Department of Electrical Engineering BP W, Sfax, 3038, Tunisia habib.dhahri@ieee.org
Abstract: This paper proposes and describes an effective utilization of the heuristic optimization. The focus of this research is on a hybrid method combining two heuristic optimization techniques; Differential evolution algorithms (DE) and particle swarm optimization (PSO), to train the beta basis function neural network (BBFNN). Denoted as PSO- DE, this hybrid technique incorporates concepts from DE and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in DE but also by mechanisms of PSO. The results of various experimental studies using the Mackey time prediction have demonstrated the superiority of the hybrid PSO-DE approach over the other four search techniques in terms of solution quality and convergence rates.
Abstract: Many methods for solving optimization problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimization methods like evolutionary algorithms, overcome this problem. In this work it is investigated how to construct a quality BBF network for a specific application can be a time-consuming process as the system must select both a suitable set of inputs and a suitable BBF network structure. Evolutionary methodologies offer the potential to automate all or part of these steps. This study illustrates how a hybrid BBFN-PSO system can be constructed, and applies the system to a number of datasets. The utility of the resulting BBFNs on these optimization problems is assessed and the results from the BBFN-PSO hybrids are shown to be competitive against the best performance on these datasets using alternative optimization methodologies. The results show that within these classes of evolutionary methods, particle swarm optimization algorithms are very robust, effective and highly efficient in solving the studied class of optimization problems
Abstract: We develop a modified differential evolution algorithm that produces radial basis function neural network controllers for chaotic systems. This method requires few controlling variables. We examine the result of applying the proposed algorithm to time series prediction, which illustrates the effectiveness of this technique. We apply this algorithm to several computational and real systems including Mackey-Glass time series, the Lorenz attractor, and experimental data obtained from the Henon map. Our experiments indicate that the structural differences between our approach and the other methods existing in the bibliography particularly are well suited to modeling chaotic time series data
