Patrick Siarry

Detection and replacement of a failing node in the wireless sensors networks

We classify connectivity maintenance approaches existing in the literature.We propose an approach for detection and replacement of a failing node.We unroll an example where we give three cases of nodes failure.The evaluation and comparison with other approach is given. The lifetime in a wireless network, in particular a wireless sensor network, depends strongly on the connectivity factor between nodes. Several factors can be at the origin of a connectivity rupture such as: lack of energy on a significant node level, infection of a vital node by a malevolent code and a logical or physical failure of a primary node. This rupture can lead in some cases to a reconfiguration of the network by generating a prejudicial overhead or in other cases to a failure of the mission assigned to the network. In this paper, we propose a DRFN approach (Detection and Replacement of a Failing Node) for the connectivity maintenance by carrying out a replacement chain according to a distributed algorithm. Through simulation, we have shown our approach efficiency. Compared with similar work, our proposed approach consumes less energy, and improves the percentage of reduction in field coverage.

Particle Swarm Optimization-based design of polynomial RST controllers

In this paper, we propose a new method for digital RST controller design based on the Particle Swarm Optimization metaheuristic. It is a systematic RST synthesis and tuning procedure to deal with the complexity of the known classical poles placement methods. The case of an electric DC drive benchmark has been successfully obtained to illustrate the efficiency of the proposed PSO-based RST control approach. Simulation results show the advantages of the designed PSO-tuned RST structure in terms of performance and robustness. A comparison with the well known Genetic Algorithm Optimization technique is investigated in order to show the superiority and effectiveness of the proposed PSO-based approach.

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 Fractional-Order Variational Framework for Retinex: Fractional-Order Partial Differential Equation-Based Formulation for Multi-Scale Nonlocal Contrast Enhancement with Texture Preserving

This paper discusses a novel conceptual formulation of the fractional-order variational framework for retinex, which is a fractional-order partial differential equation (FPDE) formulation of retinex for the multi-scale nonlocal contrast enhancement with texture preserving. The well-known shortcomings of traditional integer-order computation-based contrast-enhancement algorithms, such as ringing artefacts and staircase effects, are still in great need of special research attention. Fractional calculus has potentially received prominence in applications in the domain of signal processing and image processing mainly because of its strengths like long-term memory, nonlocality, and weak singularity, and because of the ability of a fractional differential to enhance the complex textural details of an image in a nonlinear manner. Therefore, in an attempt to address the aforementioned problems associated with traditional integer-order computation-based contrast-enhancement algorithms, we have studied here, as an interesting theoretical problem, whether it will be possible to hybridize the capabilities of preserving the edges and the textural details of fractional calculus with texture image multi-scale nonlocal contrast enhancement. Motivated by this need, in this paper, we introduce a novel conceptual formulation of the fractional-order variational framework for retinex. First, we implement the FPDE by means of the fractional-order steepest descent method. Second, we discuss the implementation of the restrictive fractional-order optimization algorithm and the fractional-order Courant–Friedrichs–Lewy condition. Third, we perform experiments to analyze the capability of the FPDE to preserve edges and textural details, while enhancing the contrast. The capability of the FPDE to preserve edges and textural details is a fundamental important advantage, which makes our proposed algorithm superior to the traditional integer-order computation-based contrast enhancement algorithms, especially for images rich in textural details.

Multi-objective optimization and energy management in renewable based AC/DC microgrid

Abstract The problems with the design of hybrid micro-grids are system price and service quality. In this paper, we solve these problems by utilizing renewable resources optimally, maintaining State of Charge (SOC) in batteries. The proposed system also defines the lowest rate for power exchanged between the AC/DC micro-grids. Photovoltaic and wind energy are utilized as key resources in the system. Also, storage banks are coupled to both micro-grids and the fuel cell is the hold-up resource to maximize the consistency of the generation system. Supervisory controller ensures the maximum utilization of resources and by maintaining SOC to manage the exchange of power between micro-grids. This research focuses on power management in AC/DC micro-grid, and its optimization has been investigated by Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The result shows that MOPSO yields positive performance and the proposed system is recommended as the best substitute to improve electric energy utilization in remote areas.

A hybrid particle swarm approach based on Tribes and tabu search for multi-objective optimization

Tuning the parameters of any evolutionary algorithm is considered as a very difficult task. In this paper, we present a new adaptive multi-objective technique which consists of a hybridization between a particular particle swarm optimization approach (Tribes) and tabu search (TS) technique. The main idea behind this hybridization is to combine the rapid convergence of Tribes with the high efficient exploitation of a local search technique based on TS. In addition, we propose three different places where the local search can be applied: TS applied on the particles of the archive, TS applied only on the best particle of each tribe and TS applied on each particle of the swarm. The aim of those propositions is to study the impact of the place where the local search is applied on the performance of our hybridized Tribes. The mechanisms proposed are validated using 10 different functions from specialized literature of multi-objective optimization. The obtained results show that using this kind of hybridization is justified as it is able to improve the quality of the solutions in the majority of cases.

Advanced Metaheuristics-Based Approach for Fuzzy Control Systems Tuning

In this study, a new advanced metaheuristics-based optimization approach is proposed and successfully applied to design and tuning of a PID-type Fuzzy Logic Controller (FLC). The scaling factors tuning problem of the FLC structure is formulated and systematically resolved, using various constrained metaheuristics such as the Differential Search Algorithm (DSA), Gravitational Search Algorithm (GSA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). In order to specify more time-domain performance control objectives of the proposed metaheuristics-tuned PID-type FLC, different optimization criteria such as Integral of Square Error (ISE) and Maximum Overshoot (MO) are considered and compared The classical Genetic Algorithm Optimization (GAO) method is also used as a reference tool to measure the statistical performances of the proposed methods. All these algorithms are implemented and analyzed in order to show the superiority and the effectiveness of the proposed fuzzy control tuning approach. Simulation and real-time experimental results, for an electrical DC drive benchmark, show the advantages of the proposed metaheuristics-tuned PID-type fuzzy control structure in terms of performance and robustness.

Computational intelligence in production and logistics systems: solving vehicle routing, supply chain network, and air-traffic trajectory planning problems [guest editorial]

The three articles in this special section focus on the use of logistics and computational intelligence in the fields of intelligent vehicle routing, supply chain management, and air traffic control trajectory planning.

Adaptive pattern search for large-scale optimization

The emergence of high-dimensional data requires the design of new optimization methods. Indeed, conventional optimization methods require improvements, hybridization, or parameter tuning in order to operate in spaces of high dimensions. In this paper, we present a new adaptive variant of a pattern search algorithm to solve global optimization problems exhibiting such a character. The proposed method has no parameters visible to the user and the default settings, determined by almost no a priori experimentation, are highly robust on the tested datasets. The algorithm is evaluated and compared with 11 state-of-the-art methods on 20 benchmark functions of 1000 dimensions from the CEC’2010 competition. The results show that this approach obtains good performances compared to the other methods tested.