A fuzzy rule base for the improved control of a pressurized waternuclear reactor

In France, nuclear energy provides about 80% of the whole electricity production. A modulation of the nuclear power plants must be able to respond to the demand on the network. The pressurized water nuclear reactor has to yield correctly a load set point. Fundamentally, two parameters are concerned in leading this task to a successful conclusion: the power axial-offset and the control rods position. The focus of this study is the automation of the control of the power axial-offset by adding soluble boron and by minimizing the volume flows through the water pump. It is also important to take into consideration the liquid waste volume. Water or boron is injected into the reactor primary circuit. At the present time this task is still performed manually by an operator, for all previous attempts to automate it failed. A nonfuzzy device, earlier developed by Electricite de France, was intensively tested at Cruas, France, power plant and allowed us to prove the feasibility of automating the boration-dilution function. But it could not be definitely adopted because it was too difficult to be tuned for industrial purposes. That device, sketchily described in the paper, gave rise to the development of a real-time fuzzy controller for the power axial-offset and the control rods insertion in a pressurized water reactor (PWR). The fuzzy controller, which is the main subject of the paper, expresses more naturally the human expertise, thus avoiding the previous issue of empirical tunings. It was implemented in simulation using Matlab-Simulink on a Sun workstation. Two realistic tests discussed show that the fuzzy controller runs as efficiently as an expert operator does     

Global Simplex Optimization—A simple and efficient metaheuristic for continuous optimization

A new hybrid optimization algorithm is proposed for minimization of continuous multi-modal functions. The algorithm called Global Simplex Optimization (GSO) is a population set based Evolutionary Algorithm (EA) incorporating a special multi-stage, stochastic and weighted version of the reflection operator of the classical simplex method. An optional mutation operator has also been tested and then removed from the structure of the final algorithm in favor of simplicity and because of insignificant effect on performance. The promising performance achieved by GSO is demonstrated by comparisons made to some other state-of-the-art global optimization algorithms over a set of conventional benchmark problems.     

A PSO-aided neuro-fuzzy classifier employing linguistic hedge concepts

The present paper proposes the development of an adaptive neuro-fuzzy classifier which employs two relatively less explored and comparatively new problem solving domains in fuzzy systems. The relatively less explored field is the domain of the fuzzy linguistic hedges which has been employed here to define the flexible shapes of the fuzzy membership functions (MFs). To achieve finer and finer adaptation, and hence control, over the fuzzy MFs, each MF is composed of several piecewise MF sections and the shape of each such MF section is varied by applying a fuzzy linguistic operator on it. The system employs a Takagi–Sugeno based neuro-fuzzy system where the rule consequences are described by zero order elements. This proposed linguistic hedge based neuro-fuzzy classifier (LHBNFC) employs a relatively new field in the area of combinatorial metaheuristics, called particle swarm optimization (PSO), for its efficient learning. PSO has been employed in this scheme to simultaneously tune the shape of the fuzzy MFs as well as the rule consequences for the entire fuzzy rule base. The performance of the proposed system is demonstrated by implementing it for two classical benchmark data sets: (i) the iris data and (ii) the thyroid data. Performance comparison vis-à-vis other available algorithms shows the effectiveness of our proposed algorithm.     

An input-delay neural-network-based approach for piecewise ECG signal compression

We propose an input delay neural network (IDNN) based time series prediction algorithm for compressing electrocardiogram (ECG) signals. Our algorithm has been tested and successfully compared vis-à-vis other popular techniques for its compression efficiency and reconstruction capability.     

Novel approach for replacement of a failure node in wireless sensor network

In the recent years, there has been a growing interest in wireless sensor networks (WSN). Network’s lifetime depends on energy efficiency and load balancing where connectivity is a very important factor. However, such connectivity can be lost due to the failure of some sensor nodes which creates disruptions to the network operations, lead to a reconfiguration of the network by generating energy losses, or in another case, the network mission fails. Energy conservation is a very important problem in WSN. In this paper, we propose a new solution for the connectivity problem when failure nodes are considered. The replacement of failed nodes is done in two phases: the first one is the search of redundant nodes using the clusterheads; the second phase is a restoration of connectivity. Performance evaluation of the proposed replacement approach shows that the results are globally satisfactory.     

A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem

The multilevel thresholding problem is often treated as a problem of optimization of an objective function. This paper presents both adaptation and comparison of six meta-heuristic techniques to solve the multilevel thresholding problem: a genetic algorithm, particle swarm optimization, differential evolution, ant colony, simulated annealing and tabu search. Experiments results show that the genetic algorithm, the particle swarm optimization and the differential evolution are much better in terms of precision, robustness and time convergence than the ant colony, simulated annealing and tabu search. Among the first three algorithms, the differential evolution is the most efficient with respect to the quality of the solution and the particle swarm optimization converges the most quickly.     

Image thresholding based on Pareto multiobjective optimization

A new image thresholding method based on multiobjective optimization following the Pareto approach is presented. This method allows to optimize several segmentation criteria simultaneously, in order to improve the quality of the segmentation. To obtain the Pareto front and then the optimal Pareto solution, we adapted the evolutionary algorithm NSGA-II (Deb et al., 2002). The final solution or Pareto solution corresponds to that allowing a compromise between the different segmentation criteria, without favouring any one. The proposed method was evaluated on various types of images. The obtained results show the robustness of the method, and its non dependence towards the kind of the image to be segmented.     

Fractional differentiation and non-Pareto multiobjective optimization for image thresholding

Various techniques have previously been proposed for single-stage thresholding of images to separate objects from the background. Although these global or local thresholding techniques have proven effective on particular types of images, none of them is able to produce consistently good results on a wide range of existing images. Here, a new image histogram thresholding method, called TDFD, based on digital fractional differentiation is presented for gray-level image thresholding. The proposed method exploits the properties of the digital fractional differentiation and is based on the assumption that the pixel appearance probabilities in the image are related. To select the best fractional differentiation order that corresponds to the best threshold, a new algorithm based on non-Pareto multiobjective optimization is presented. A new geometric regularity criterion is also proposed to select the best thresholded image. In order to illustrate the efficiency of our method, a comparison was performed with five competing methods: the Otsu method, the Kapur method, EM algorithm based method, valley emphasis method, and two-dimensional Tsallis entropy based method. With respect to the mode of visualization, object size and image contrast, the experimental results show that the segmentation method based on fractional differentiation is more robust than the other methods.     

A combinatorial particle swarm optimisation for solving permutation flowshop problems

The m-machine permutation flowshop problem PFSP with the objectives of minimizing the makespan and the total flowtime is a common scheduling problem, which is known to be NP-complete in the strong sense, when m ? 3. This work proposes a new algorithm for solving the permutation FSP, namely combinatorial Particle Swarm Optimization. Furthermore, we incorporate in this heuristic an improvement procedure based on the simulated annealing approach. The proposed algorithm was applied to well-known benchmark problems and compared with several competing metaheuristics.