scholarly journals Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3231 ◽  
Author(s):  
Shirin Tahmasebi ◽  
Mohadeseh Safi ◽  
Somayeh Zolfi ◽  
Mohammad Reza Maghsoudi ◽  
Hamid Reza Faragardi ◽  
...  
Keyword(s):  
Network Performance ◽  
Optimization Problem ◽  
Average Distance ◽  
Research Problem ◽  
Global Optimum ◽  
Optimal Placement ◽  
Cuckoo Optimization Algorithm ◽  
Pc Algorithm ◽  
Synchronization Overhead ◽  
And Performance

Due to reliability and performance considerations, employing multiple software-defined networking (SDN) controllers is known as a promising technique in Wireless Sensor Networks (WSNs). Nevertheless, employing multiple controllers increases the inter-controller synchronization overhead. Therefore, optimal placement of SDN controllers to optimize the performance of a WSN, subject to the maximum number of controllers, determined based on the synchronization overhead, is a challenging research problem. In this paper, we first formulate this research problem as an optimization problem, then to address the optimization problem, we propose the Cuckoo Placement of Controllers (Cuckoo-PC) algorithm. Cuckoo-PC works based on the Cuckoo optimization algorithm which is a meta-heuristic algorithm inspired by nature. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. To evaluate the performance of Cuckoo-PC, we compare it against a couple of state-of-the-art methods, namely Simulated Annealing (SA) and Quantum Annealing (QA). The experiments demonstrate that Cuckoo-PC outperforms both SA and QA in terms of the network performance by lowering the average distance between sensors and controllers up to 13% and 9%, respectively. Comparing our method against Integer Linear Programming (ILP) reveals that Cuckoo-PC achieves approximately similar results (less than 1% deviation) in a noticeably shorter time.

scholarly journals An Optimization Method for the Configuration of Inter Array Cables for Floating Offshore Wind Farm

2017 ◽  
Author(s):  
Yann Poirette ◽  
Martin Guiton ◽  
Guillaume Huwart ◽  
Delphine Sinoquet ◽  
Jean Marc Leroy
Keyword(s):  
Wind Farm ◽  
Optimization Problem ◽  
Initial Point ◽  
Trust Region ◽  
Optimization Method ◽  
Global Optimum ◽  
Offshore Wind ◽  
Finite Element Software ◽  
Constrained Problems ◽  
Floating Offshore Wind Turbines

IFP Energies nouvelles (IFPEN) is involved for many years in various projects for the development of floating offshore wind turbines. The commercial deployment of such technologies is planned for 2020. The present paper proposes a methodology for the numerical optimization of the inter array cable configuration. To illustrate the potential of such an optimization, results are presented for a case study with a specific floating foundation concept [1]. The optimization study performed aims to define the least expensive configuration satisfying mechanical constraints under extreme environmental conditions. The parameters to be optimized are the total length, the armoring, the stiffener geometry and the buoyancy modules. The insulated electrical conductors and overall sheath are not concerned by this optimization. The simulations are carried out using DeepLines™, a Finite Element software dedicated to simulate offshore floating structures in their marine environment. The optimization problem is solved using an IFPEN in-house tool, which integrates a state of the art derivative-free trust region optimization method extended to nonlinear constrained problems. The latter functionality is essential for this type of optimization problem where nonlinear constraints are introduced such as maximum tension, no compression, maximum curvature and elongation, and the aero-hydrodynamic simulation solver does not provide any gradient information. The optimization tool is able to find various local feasible extrema thanks to a multi-start approach, which leads to several solutions of the cable configuration. The sensitivity to the choice of the initial point is demonstrated, illustrating the complexity of the feasible domain and the resulting difficulty in finding the global optimum configuration.

Adaptive Complex Method for Efficient Design Optimization

2007 ◽  
Author(s):  
Marcus Pettersson ◽  
Johan O¨lvander
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Global Optimum ◽  
Direct Search ◽  
Complex Method ◽  
Efficient Design ◽  
Direct Search Methods ◽  
Simulation Based ◽  
Set Of Points ◽  
Quasi Newton

Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

Optimal Placement of Horizontal- and Vertical-Axis Wind Turbines in a Wind Farm for Maximum Power Generation Using a Genetic Algorithm

2011 ◽  
Author(s):  
Xiaomin Chen ◽  
Ramesh Agarwal
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbines ◽  
Wind Farm ◽  
Optimization Problem ◽  
Vertical Axis ◽  
Optimal Placement ◽  
Vertical Axis Wind Turbines ◽  
Wake Effects ◽  
Horizontal Axis Wind Turbines ◽  
Wake Model

In this paper, we consider the Wind Farm layout optimization problem using a genetic algorithm. Both the Horizontal–Axis Wind Turbines (HAWT) and Vertical-Axis Wind Turbines (VAWT) are considered. The goal of the optimization problem is to optimally place the turbines within the wind farm such that the wake effects are minimized and the power production is maximized. The reasonably accurate modeling of the turbine wake is critical in determination of the optimal layout of the turbines and the power generated. For HAWT, two wake models are considered; both are found to give similar answers. For VAWT, a very simple wake model is employed.

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

2021 ◽  
Vol 12 (4) ◽  
pp. 98-116
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Local Optima ◽  
Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

scholarly journals Phasor Measurement Units Optimal Placement and Performance Limits for Fault Localization

2020 ◽  
Vol 38 (1) ◽  
pp. 180-192 ◽  
Author(s):  
Mahdi Jamei ◽  
Raksha Ramakrishna ◽  
Teklemariam Tesfay ◽  
Reinhard Gentz ◽  
Ciaran Roberts ◽  
...  
Keyword(s):  
Fault Localization ◽  
Phasor Measurement Units ◽  
Optimal Placement ◽  
Performance Limits ◽  
Phasor Measurement ◽  
Measurement Units ◽  
And Performance

scholarly journals A Multiple Mitosis Genetic Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 252
Author(s):  
K. Kamil ◽  
K.H Chong ◽  
H. Hashim ◽  
S.A. Shaaya
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Total Population ◽  
Global Optimum ◽  
Natural Process ◽  
Local Optimum ◽  
Simple Genetic Algorithm ◽  
Cell Reproduction ◽  
Solve Optimization Problem ◽  
High Diversity

<p>Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum.  This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.</p>

scholarly journals An Instructor Learning Analytics Implementation Model

2017 ◽  
Vol 21 (3) ◽  
Author(s):  
Holly McKee
Keyword(s):  
Learning Outcomes ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Research Problem ◽  
Self Reflection ◽  
Implementation Model ◽  
Design And Development Research ◽  
Pedagogical Intent ◽  
And Performance ◽  
Support Students

With the widespread use of learning analytics tools, there is a need to explore how these technologies can be used to enhance teaching and learning. Little research has been conducted on what human processes are necessary to facilitate meaningful adoption of learning analytics. The research problem is that there is a lack of evidence-based guidance on how instructors can effectively implement learning analytics to support students with the purpose of improving learning outcomes. The goal was to develop and validate a model to guide instructors in the implementation of learning analytics tools. Using design and development research methods, an implementation model was constructed and validated internally. Themes emerged falling into the categories of adoption and caution with six themes falling under adoption including: LA as evidence, reaching out, frequency, early identification/intervention, self-reflection, and align LA with pedagogical intent and three themes falling under the category of caution including: skepticism, fear of overdependence, and question of usefulness.  The model should enhance instructors’ use of learning analytics by enabling them to better take advantage of available technologies to support teaching and learning in online and blended learning environments. Researchers can further validate the model by studying its usability (i.e., usefulness, effectiveness, efficiency, and learnability), as well as, how instructors’ use of this model to implement learning analytics in their courses affects retention, persistence, and performance.

Online Assortment Optimization with Reusable Resources

2021 ◽  
Author(s):  
Xiao-Yue Gong ◽  
Vineet Goyal ◽  
Garud N. Iyengar ◽  
David Simchi-Levi ◽  
Rajan Udwani ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Online Algorithm ◽  
Random Number ◽  
Optimal Algorithm ◽  
User Preference ◽  
Substitutable Products ◽  
Assortment Optimization ◽  
Myopic Policy ◽  
Expected Revenue ◽  
And Performance

We consider an online assortment optimization problem where we have n substitutable products with fixed reusable capacities [Formula: see text]. In each period t, a user with some preferences (potentially adversarially chosen) who offers a subset of products, St, from the set of available products arrives at the seller’s platform. The user selects product [Formula: see text] with probability given by the preference model and uses it for a random number of periods, [Formula: see text], that is distributed i.i.d. according to some distribution that depends only on j generating a revenue [Formula: see text] for the seller. The goal of the seller is to find a policy that maximizes the expected cumulative revenue over a finite horizon T. Our main contribution is to show that a simple myopic policy (where we offer the myopically optimal assortment from the available products to each user) provides a good approximation for the problem. In particular, we show that the myopic policy is 1/2-competitive, that is, the expected cumulative revenue of the myopic policy is at least half the expected revenue of the optimal policy with full information about the sequence of user preference models and the distribution of random usage times of all the products. In contrast, the myopic policy does not require any information about future arrivals or the distribution of random usage times. The analysis is based on a coupling argument that allows us to bound the expected revenue of the optimal algorithm in terms of the expected revenue of the myopic policy. We also consider the setting where usage time distributions can depend on the type of each user and show that in this more general case there is no online algorithm with a nontrivial competitive ratio guarantee. Finally, we perform numerical experiments to compare the robustness and performance of myopic policy with other natural policies. This paper was accepted by Gabriel Weintraub, revenue management and analytics.

Traffic and Network Performance Monitoring for Effective Quality of Service and Network Management

2010 ◽  
pp. 201-227
Author(s):  
P. Papantoni-Kazakos ◽  
A. T. Burrell
Keyword(s):  
Mobile Networks ◽  
Network Performance ◽  
Performance Monitoring ◽  
Sequential Algorithm ◽  
Heterogeneous Environments ◽  
Time Varying ◽  
Distributed Environment ◽  
Data Flows ◽  
Network Performance Monitoring ◽  
And Performance

The authors consider distributed mobile networks carrying time-varying heterogeneous traffics. To deal effectively with the mobile and time-varying distributed environment, the deployment of traffic and network performance monitoring techniques is necessary for the identification of traffic changes, network failures, and also for the facilitation of protocol adaptations and topological modifications. Concurrently, the heterogeneous traffic environment necessitates the deployment of hybrid information transport techniques. This chapter discusses the design, analysis, and evaluation of distributed and dynamic techniques which manage the traffic and monitor the network performance effectively, while capturing the dynamics inherent in the mobile heterogeneous environments. Specifically, the design of a monitoring sub-network is sought, where the arising research tasks include: • the adoption of a core sequential algorithm which monitors both the variations in the rates of the information data flows and the dynamics of the network performance. • The identification of the specific operational and performance characteristics of the monitoring systems, when the core algorithm is implemented in a distributed environment; for a given network topology, it is important to identify the minimum size monitoring sub-network for complete “visibility” of data flows and network functions. • Dynamically changing monitoring sub-network architectures, as functions of time-varying network topologies. • The deployment of Artificial Intelligence learning techniques, in the presence of dynamically changing network and information flow environments, to appropriately adapt crucial operational parameters of the data monitoring algorithms.

scholarly journals Optimizations for Energy Efficiency in Software-Defined Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4779
Author(s):  
Sorin Buzura ◽  
Bogdan Iancu ◽  
Vasile Dadarlat ◽  
Adrian Peculea ◽  
Emil Cebuc
Keyword(s):  
Energy Efficiency ◽  
Network Performance ◽  
Wireless Sensor ◽  
Management Approach ◽  
Control Plane ◽  
Data Broadcast ◽  
Data Packets ◽  
Similar Work ◽  
And Performance ◽  
The Individual

Software-defined wireless sensor networking (SDWSN) is an emerging networking architecture which is envisioned to become the main enabler for the internet of things (IoT). In this architecture, the sensors plane is managed by a control plane. With this separation, the network management is facilitated, and performance is improved in dynamic environments. One of the main issues a sensor environment is facing is the limited lifetime of network devices influenced by high levels of energy consumption. The current work proposes a system design which aims to improve the energy efficiency in an SDWSN by combining the concepts of content awareness and adaptive data broadcast. The purpose is to increase the sensors’ lifespan by reducing the number of generated data packets in the resource-constrained sensors plane of the network. The system has a distributed management approach, with content awareness being implemented at the individual programmable sensor level and the adaptive data broadcast being performed in the control plane. Several simulations were run on historical weather and the results show a significant decrease in network traffic. Compared to similar work in this area which focuses on improving energy efficiency with complex algorithms for routing, clustering, or caching, the current proposal employs simple computing procedures on each network device with a high impact on the overall network performance.

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