Eating Disorders and Obesity: a Comprehensive Handbook

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Computers And Intractability A Guide To The Theory Of Np Completeness

https://repub.eur.nl/pub/134773/Repub_134773_O-A.pdf

Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda

In recent years, Software Defined Networking (SDN) has emerged as a pivotal element not only in data-centers and wide-area networks, but also in next generation networking architectures such as Vehicular ad hoc network and 5G. SDN is characterized by decoupled data and control planes, and logically centralized control plane. The centralized control plane in SDN offers several opportunities as well as challenges. A key design choice of the SDN control plane is placement of the controller(s), which impacts a wide range of network issues ranging from latency to resiliency, from energy efficiency to load balancing, and so on. In this paper, we present a comprehensive survey on the controller placement problem (CPP) in SDN. We introduce the CPP in SDN and highlight its significance. We present the classical CPP formulation along with its supporting system model. We also discuss a wide range of the CPP modeling choices and associated metrics. We classify the CPP literature based on the objectives and methodologies. Apart from the primary use-cases of the CPP in data-center networks and wide area networks, we also examine the recent application of the CPP in several new domains such as mobile/cellular networks, 5G, named data networks, wireless mesh networks and VANETs. We conclude our survey with discussion on open issues and future scope of this topic.

A Survey on Controller Placement in SDN

Recently, many optimization algorithms have been applied for Feature selection (FS) problems and show a clear outperformance in comparison with traditional FS methods. Therefore, this has motivated our study to apply the new Salp swarm algorithm (SSA) on the FS problem. However, SSA, like other optimizations algorithms, suffer from the problem of population diversity and fall into local optima. To solve these problems, this study presents an enhanced version of SSA which is known as the Dynamic Salp swarm algorithm (DSSA). Two main improvements were included in SSA to solve its problems. The first improvement includes the development of a new equation for salps’ position update. The use of this new equation is controlled by using Singer's chaotic map. The purpose of the first improvement is to enhance SSA solutions' diversity. The second improvement includes the development of a new local search algorithm (LSA) to improve SSA exploitation. The proposed DSSA was combined with the K-nearest neighbor (KNN) classifier in a wrapper mode. 20 benchmark datasets were selected from the UCI repository and 3 Hadith datasets to test and evaluate the effectiveness of the proposed DSSA algorithm. The DSSA results were compared with the original SSA and four well-known optimization algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Lion Optimizer (ALO), and Grasshopper Optimization Algorithm (GOA). From the obtained results, DSSA outperformed the original SSA and the other well-known optimization algorithms over the 23 datasets in terms of classification accuracy, fitness function values, the number of selected features, and convergence speed. Also, DSSA accuracy results were compared with the most recent variants of the SSA algorithm. DSSA showed a significant improvement over the competing algorithms in statistical analysis. These results confirm the capability of the proposed DSSA to simultaneously improve the classification accuracy while selecting the minimal number of the most informative features.

Dynamic Salp swarm algorithm for feature selection

Determination of the optimal sales level of perishable goods in a two-echelon supply chain network

SDN is a promising architecture that can overcome the challenges facing traditional networks. SDN enables administrator/operator to build a simpler, customizable, programmable, and manageable network. In software-defined WAN deployments, multiple controllers are often needed, and the location of these controllers affect various metrics. Since these metrics conflict each other, this problem can be regarded as a multi-objective combinatorial optimization problem (MOCO). A particular efficient method to solve a typical MOCO, which is used in the relevant literature, is to find the actual Pareto frontier first and give it to the decision maker to select the most appropriate solution(s). In small and medium sized combinatorial problems, evaluating the whole search space and find the exact Pareto frontier may be possible in a reasonable time. However, for large scale problems whose search spaces involves thousands of millions of solutions, the exhaustive evaluation needs a considerable amount of computational efforts and memory used. An effective alternative mechanism is to estimate the original Pareto frontier within a relatively small algorithm's runtime and memory consumption. Heuristic methods, which have been studied well in the literature, proved to be very effective methods in this regards. The second version of the Non-dominated Sorting Genetic Algorithm, called NSGA-II has been carried out quite well on different discrete and continuous optimization problems. In this paper, we adapt this efficient mechanism for a new presented multi-objective model of the control placement problem [7]. The results of implementing the adapted algorithm carried out on the Internet2 OS3E network run on MATLAB 2013b confirmed its effectiveness.

Controller placement in software-defined WAN using multi objective genetic algorithm

In this paper, the no-wait flow shop scheduling problem under makespan and flowtime criteria is addressed. The no-wait flowshop is a variant of the well-known flowshop scheduling problem where all processes follow the previous one without any interruption for operations of a job. Owing to the problem is known to be NP-hard for more than two machines, a hybrid meta-heuristic algorithm based on ant colony optimization (ACO) and simulated annealing (SA) algorithm is improved. First, at each step, due to the characteristic of ACO algorithm that include solution construction and pheromone trail updating, some different areas of search space are checked and best solution is selected. Then, to enhance the quality and diversity of the solution and finding best neighbor of this solution, a novel SA is presented. Moreover, a new principle is applied for global pheromone update based on the probability function like SA algorithm. The proposed approach solution is compared with several the state-of-the-art algorithms in the literature. The reported results show that the proposed algorithms are effective and the new approach for local search in ACO algorithm is efficient for solving the no-wait flow shop problem. Then, we employed another hybrid ACO algorithm based on hybridization of ACO with variable neighborhood search (VNS) and compare the results given by two proposed algorithms. These results show that our new hybrid provides better results than ACO-VNS algorithm.

/pdf/a-new-hybrid-ant-colony-algorithm-for-scheduling-of-no-wait-4l00hlbhso.pdf

Morteza Kazemi

Papers

Determination of the optimal sales level of perishable goods in a two-echelon supply chain network

Controller placement in software-defined WAN using multi objective genetic algorithm

A new hybrid ant colony algorithm for scheduling of no-wait flowshop

Ant colony algorithm for the shortest loop design problem

Covering and connectivity constraints in loop-based formulation of material flow network design in facility layout