Ant systems & Local Search Optimization for flexible Job Shop Scheduling Production

The problem of efficiently scheduling production jobs on several machines is an important consideration when attempting to make effective use of a multimachines system such as a flexible job shop scheduling production system (FJSP). In most of its practical formulations, the FJSP is known to be NP-hard [8][9], so exact solution methods are unfeasible for most problem instances and heuristic approaches must therefore be employed to find good solutions with reasonable search time. In this paper, two closely related approaches to the resolution of the flexible job shop scheduling production system are described. These approaches combine the Ant system optimisation meta-heuristic (AS) with local search methods, including tabu search. The efficiency of the developed method is compared with others

Improvement of DV-Hop Localization Algorithm in Multi-hop Wireless Sensor Networks

International audienceLocalization in wireless sensor networks is used to track and analyze information sensed by nodes. Localization techniques typically estimate node position based on a set of sensor nodes, denoted as anchors, that are aware of theirgeographic positions. Many localization algorithms are proposed in the literature, mainly using the Distance Vector Hop algorithms (DV-Hop) and its many improvements. In this paper, we propose an optimized method to compute the average distance of hops between sensor nodes, namely the HopSize. This approach is an improvement of the DV-Hop algorithm as it allows estimating with accuracy the position of nodes in the network. We focus on range-free localization algorithms in homogeneous multihop wireless sensor networks. Simulation results show that our approach significantly reduces the average error of nodes estimated positions compared with the original DV-Hop as well as an improved localization algorithm from the literature

Tuning PID Controller Using Multiobjective Ant Colony Optimization

This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (Kp, Ki, and Kd) by minimizing the multiobjective function. The potential of using multiobjective ant algorithms is to identify the Pareto optimal solution. The other methods are applied to make comparisons between a classic approach based on the “Ziegler-Nichols” method and a metaheuristic approach based on the genetic algorithms. Simulation results demonstrate that the new tuning method using multiobjective ant colony optimization has a better control system performance compared with the classic approach and the genetic algorithms

Logic Gate-based Evolutionary Algorithm for the multidimensional knapsack problem-wireless sensor network application

Evolutionary algorithms (EAs) are predominantly employed to find solutions for continuous optimization problems. As EAs are initially presented for continuous spaces, research on extending EAs to find solutions for binary spaces is in growing concern. In this paper, a logic gate-based evolutionary algorithm (LGEA) for solving some combinatorial optimization problems (COPs) is introduced. The proposed LGEA has the following features. First, it employs the logic operation to generate the trial population. Thereby, LGEA replaces common space transformation rules and classic recombination and mutation methods. Second, it is based on exploiting a variety of logic gates to search for the best solution. The variety among these logic tools will naturally lead to promote diversity in the population and improve global search abilities. The LGEA presents thus a new technique to combine the logic gates into the procedure of generating offspring in an evolutionary context. To judge the performance of the algorithm, we have solved the NP-hard multidimensional knapsack problem as well as a well-known engineering optimization problem, task allocation for wireless sensor network. Experimental results show that the proposed LGEA is promising

Hop-based routing protocol based on energy efficient Minimum Spanning Tree for wireless sensor network

International audienceA wireless sensor network (WSN) is consisting of a set of sensor nodes with a limited energy stored in their batteries. Generally, replacing or charging the battery is hard and inefficient. Further, the main critical aspect of applications based on wireless sensor networks is their lifetime. Therefore, judicious power management with optimized routing protocols can effectively optimize the energy consumption of sensor nodes and thus extend the network lifetime. In this paper, an optimized routing protocol for wireless sensor nodes is proposed. We are interested in constructing an efficient routing spanning tree that minimizes the energy consumption among all nodes in the network and fit for WSN with reduced energy for achieving a longer lifetime. The main idea of this algorithm comes from the Minimum Spanning Tree (MST) graph theory. This approach focuses on the minimal hop count of each node to reach the destination (sink node) within an optimal path