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A new energy-aware technique to improve the network lifetime of wireless Internet of Things using a most valuable player algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第6期27卷 7251-7271页

作者： Xiao, Yongjun Voronkova, Daria K. Hubei Engn Univ Sch Phys & Elect Informat Engn Xiaogan 432000 Peoples R China Gulf Univ Sci & Technol Dept Math & Nat Sci Mishref Campus Kuwait 32093 Kuwait Bauman Moscow State Tech Univ Moscow Russia

With the fast evolution of Internet of Things (IoT) applications, Wireless Sensor Networks (WSNs) have become a crucial part of modern infrastructure. The efficient provision of services and wiser use of resources are currently of great importance. WSNs consist of several sensor nodes that collaborate to monitor and send data to a central location known as a sink. The sink, also called a base station, serves as the endpoint for data transmission in each round. However, due to the limited computation, storage, and energy resources of sensor nodes, they often face challenges in changing clusters. Optimal selection of a node, aimed at minimizing network fragmentation and enhancing energy utilization, necessitates a sophisticated evaluation and computational procedure, demanding a substantial energy investment. Subsequently, the task at hand is the development of a system facilitating the connection of remote sensing sources to WSNs with minimal energy consumption. The primary goals of WSNs based on the IoT revolve around extending network longevity and enhancing energy efficiency. In the realm of IoT-based WSNs, where the efficiency of data collection and management is paramount, cluster-based methodologies have demonstrated their effectiveness. This investigation proposes the implementation of a most valuable player algorithm (MVPA) specifically tailored for IoT-based WSNs, taking into account diverse factors influencing node energy and network lifespan. The MVPA is a highly competitive optimization method that converges faster (with fewer function evaluations) and has a greater overall success rate. In this case, the optimum cluster head for an IoT-based WSN was chosen using an MVPA to maximize energy savings. Simulation results demonstrate that the recommended strategy, when compared to other current methods, increases the network lifetime by using the minimum amount of energy needed to function.

关键词： IoT-based WSN Cluster-head Energy Optimization algorithm most valuable player algorithm

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most valuable player algorithm: a novel optimization algorithm inspired from sport

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OPERATIONAL RESEARCH 2020年第1期20卷 139-195页

作者： Bouchekara, H. R. E. H. Univ Freres Mentouri Constantine Dept Elect Engn Lab Elect Engn Constantine LGEC Constantine 25000 Algeria

In this paper a new metaheuristic called the most valuable player algorithm (MVPA) is proposed for solving optimization problems. The developed algorithm is inspired from sport where players form teams, then these players compete collectively (in teams) in order to win the championship and they compete also individually in order to win the MVP trophy. The performances of MVPA are evaluated on a set of 100 mathematical test functions. The obtained results are compared with the ones obtained using 13 well-known optimization algorithms. These results demonstrate that, the MVPA is a very competitive optimization algorithm, it converges rapidly (with smaller number of functions evaluations) and more successfully (with higher overall success percentage) than the compared algorithms. Therefore, further developments and applications of MVPA would be worth investigating in future studies.

关键词： Optimization Metaheuristic most valuable player algorithm Sport

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An enhanced most valuable player algorithm based optimal power flow using Broyden's method

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2020年 42卷

作者： Srilakshmi, Koganti Babu, P. Ravi Aravindhababu, P. Sreenidhi Inst Sci & Technol Dept Elect Engn Hyderabad India Annamalai Univ Dept Elect Engn Annamalainagar Tamil Nadu India

Optimal Power Flow (OPF) problem is an important operational problem in power systems with a goal of optimizing one or more chosen objectives through adjusting certain control parameters. The most valuable player algorithm (MVPA) is a population based approach, inspired from the behaviour of players with a goal of winning the trophy, for solving optimization problems. This paper firstly enhances the MVPA by taking into account the bad and offensive players with a goal of avoiding sub-optimal traps and achieving a fast convergence, and then suggests a new solution method involving the enhanced MVPA for solving the OPF problem. The method employs a fast Newton-Raphson power flow involving Broyden's method for evaluating the performance of the players. It presents simulation results of IEEE 30 and 57 bus test systems and exhibits that the suggested method is robust and efficient.

关键词： Optimal power flow most valuable player algorithm Broyden's method Power flow analysis Voltage stability

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most valuable player algorithm for Solving Minimum Vertex Cover Problem

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第8期10卷 159-167页

作者： Khattab, Hebatullah Sharieh, Ahmad Mahafzah, Basel A. Univ Jordan King Abdulla II Sch Informat & Technol Dept Comp Sci Amman Jordan

Minimum Vertex Cover Problem (MVCP) is a combinatorial optimization problem that is utilized to formulate multiple real-life applications. Owing to this fact, abundant research has been undertaken to discover valuable MVCP solutions. most valuable player algorithm (MVPA) is a recently developed metaheuristic algorithm that inspires its idea from team-based sports. In this paper, the MVPA_MVCP algorithm is introduced as an adaptation of the MVPA for the MVCP. The MVPA_MVCP algorithm is implemented using Java programming language and tested on a Microsoft Azure virtual machine. The performance of the MVPA_MVCP algorithm is evaluated analytically in terms of run time complexity. Its average-case run time complexity is ceased to Theta(I(vertical bar V vertical bar + vertical bar E vertical bar)), where I is the size of the initial population, vertical bar V vertical bar is the number of vertices and vertical bar E vertical bar is the number of edges of the tested graph. The MVPA_MVCP algorithm is evaluated experimentally in terms of the quality of gained solutions and the run time. The experimental results over 15 instances of DIMACS benchmark revealed that the MVPA_MVCP algorithm could, in the best case, get the best known optimal solution for seven data instances. Also, the experimental findings exposed that there is a direct relation between the number of edges of the graph under test and the run time.

关键词： most valuable player algorithm minimum vertex cover problem metaheuristic algorithms optimization problem

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An Improved most valuable player algorithm with Twice Training Mechanism 14th

An Improved Most Valuable Player Algorithm with Twice Traini...

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14th International Conference on Intelligent Computing (ICIC)

作者： Liu, Xin Luo, Qifang Wang, Dengyun Abdel-Baset, Mohamed Jiang, Shengqi Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Nanning 530006 Peoples R China Zagazig Univ Fac Comp & Informat El Zera Sq Zagazig 44519 Sharqiyah Egypt

ISBN: (纸本)9783319959290;9783319959306

The most valuable player algorithm is inspired from these players who want to win the most valuable player (MVP) trophy, it have higher overall success percentage. Teaching-learning-based optimization (TLBO) simulates the process of teaching and learning. TLBO has fewer parameters that must be determined during the renewal process. This paper proposes twice training mechanism to enhance the search ability of the most valuable player algorithm (MVPA) through hybrid TLBO algorithm, and named it teaching the most valuable player algorithm (TMVPA). In TMVPA, designs two behaviors of training and abstract two training modes: pre-competition training and post-competition training. Before individual competition, join the pre-competition training to coordinated exploitation ability and the exploration ability of the original algorithm and join the post-competition training to prevent from falling into the local optimal field after the corporate competition. We test three benchmark functions and an engineering design problem. Results show that TMVPA has effectively raised algorithm accuracy.

关键词： most valuable player algorithm Two training modes Benchmark functions Teaching-learning-based optimization Engineering design problems

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A hybrid algorithm based on modified chemical reaction optimization and best-first search algorithm for solving minimum vertex cover problem

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NEURAL COMPUTING & APPLICATIONS 2022年第18期34卷 15513-15541页

作者： Khattab, Hebatullah Mahafzah, Basel A. Sharieh, Ahmad Al Balqa Appl Univ Fac Artificial Intelligence Autonomous Syst Dept Al Balqa Jordan Univ Jordan King Abdullah II Sch Informat Technol Comp Sci Dept Amman 11942 Jordan

The minimum vertex cover problem (MVCP) is one of the well-known NP-complete problems that can be used to formulate numerous real-life applications. The MVCP has been solved using different approaches, exact, heuristic, and metaheuristic. Chemical reaction optimization (CRO) is a population-based metaheuristic algorithm that simulates what happens in chemical reactions to solve many problems. In this paper, the MVCP is solved using hybridization of a modified version of the CRO algorithm and the best-first search (BFS) algorithm. This hybridization is symbolized by MCROA-BFS. The BFS algorithm is exploited to generate initial populations with high-quality initial solutions in comparison with the random bit-vector (RBV) approach as a traditional approach for generating initial populations for several population-based metaheuristic algorithms. At first, the MCROA is evaluated analytically in terms of run time complexity. Then the performance of the MCROA is evaluated experimentally to show the effectiveness of exploiting the BFS in terms of quality of gained solutions and run time. The most valuable player algorithm (MVPA), genetic algorithm (GA), and hybrid CRO algorithm (HCROA) are metaheuristic algorithms that used the RBV approach to generate the initial population. MCROA-BFS is compared, under the selected performance metrics, with these metaheuristic algorithms in addition to MCROA with the RBV approach (MCROA-RBV). The conducted experiments were carried out using 18 instances of DIMACS and BHOSLIB benchmarks. The experimental results revealed the superiority of MCROA-BFS over MVPA, GA, and MCROA-RBV in terms of both performance metrics. Although HCROA has the smallest run time, it failed to obtain high-quality solutions in comparison with MCROA.

关键词： Minimum vertex cover problem Optimization problem Metaheuristic algorithm Chemical reaction optimization Genetic algorithm most valuable player algorithm

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New strategy to control covid-19 pandemic using lead/lag compensator

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期68卷 102669-102669页

作者： Hadi, Musadaq A. Amean, Zainab M. Univ Technol Baghdad Baghdad Iraq Univ Southern Queensland Toowoomba Qld Australia

COVID-19 is still the main worldwide issue since the outbreak. Many strategies were implemented such as suppression, mitigation, and mathematical-engineering strategies, to control this pandemic. In this work, a lead/ lag compensator is proposed to control an unstable Covid-19 nonlinear system after using some required assumptions. The control theory is involved with the unstable pandemic and other existing strategies until the invention of the vaccine is approved. In addition, the most valuable player algorithm (MVPA) is used to optimize the parameters of the proposed controller and to determine whether it is a lead or lag compensator. Finally, the simulation results are based on the daily reports of two pandemic cities: Hubei (China), and Lazio (Italy) since the outbreak began. It can be concluded that the lead/lag compensator can effectively control the COVID-19 system.

关键词： Lag compensator Lead compensator COVID-19 Nonlinear system Coronavirus most valuable player algorithm

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Control of COVID-19 system using a novel nonlinear robust control algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年 64卷 102317-102317页

作者： Hadi, Musadaq A. Ali, Hazem, I Univ Technol Baghdad Control & Syst Engn Dept Baghdad Iraq

COVID-19 has been a worldwide concern since the outbreak. Many strategies have been involved such as suppression and mitigation strategies to deal with this epidemic. In this paper, a new mathematical-engineering strategy is introduced in order to control the COVID-19 epidemic. Thereby, control theory is involved in controlling the unstable epidemic alongside with the other suggested strategies until the vaccine will hopefully be invented as soon as possible. A new robust control algorithm is introduced to compensate the COVID-19 nonlinear system by propose a proper controller after using necessary assumptions and analysis are made. In addition, the Variable Transformation Technique (VTT) is used to simplify the COVID-19 system. Furthermore, the most valuable player algorithm (MVPA) is applied in order to optimize the parameters of the proposed controller. The simulation results are based on the daily reports of two cities Hubei (China) and Lazio (Italy) since the outbreak. It can be concluded that the proposed control algorithm can effectively compensate the COVID-19 system. In addition, it can be considered as an effective mathematical-engineering strategy to control this epidemic alongside with the other strategies.

关键词： Robust control algorithm COVID-19 Nonlinear system Coronavirus Variable Transformation Technique most valuable player algorithm

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Maximization of Solar Radiation on PV Panels With Optimal Intervals and Tilt Angle: Case Study of Yanbu, Saudi Arabia

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Ramli, Makbul A. M. Bouchekara, Houssem R. E. H. Shahriar, Mohammad S. Milyani, Ahmad H. Rawa, Muhyaddin King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah Saudi Arabia King Abdulaziz Univ Dept Elect & Comp Engn Jeddah Saudi Arabia Univ Hafr Al Batin Dept Elect Engn Hafar al Batin Saudi Arabia

A key factor in the performance of PV panels is the tilt angle, adjustable via various tracking systems. Fixed tilt angle PV panels miss out on most of the solar radiation each day whereas continuous tracking systems are not always cost-efficient, rather impractical in some cases. Therefore, adjusting the tilt angle using a limited number of periods per year can be a good, compromised solution. In this paper, a new approach is proposed to maximize the impact of solar radiation on PV panels by adjusting their tilt angles. Based on a limited number of periods or intervals per year, the optimal duration (number of days) of each period or interval along with the optimum tilt angle corresponding to each interval are determined by solving two interlinked optimization problems. These two problems are solved using the most valuable player algorithm (MVPA) combined with the Particle Swarm Optimization (PSO) algorithm. The case study for Yanbu, a western coastal city of Saudi Arabia has been investigated. The obtained results show that the enhanced solar power generation can be achieved by using optimal intervals and optimum tilt angle and provide a suitable benchmark for similar techniques to be used in the future to solve the problem of tilt angle adjustment for maximizing PV panels output.

关键词： optimal intervals optimum tilt angle solar panel solar radiation most valuable player algorithm particle swarm optimization

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Optimal Sizing and Location of Distributed Generators for Power Flow Analysis in Smart Grid Using IAS-MVPA Strategy

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2021年第4期20卷

作者： Cherukupalli, Kumar Anand, Vijaya N. Prasad V Potluri Siddhartha Inst Technol Dept Elect & Elect Engn Vijayawada Andhra Pradesh India

In this paper, the optimal distribution generation (DG) size and location for power flow analysis at the smart grid by hybrid method are proposed. The proposed hybrid method is the Interactive Autodidactic School (IAS) and the most valuable player algorithm (MVPA) and commonly named as IAS-MVPA method. The main aim of this work is to reduce line loss and total harmonic distortion (THD), similarly, to recover the voltage profile of system through the optimal location and size of the distributed generators and optimal rearrangement of network. Here, IAS-MVPA method is utilized as a rectification tool to get the maximum DG size and the maximal reconfiguration of network at environmental load variation. In case of failure, the IAS method is utilized for maximizing the DG location. The IAS chooses the line of maximal power loss as optimal location to place the DG based on the objective function. The fault violates the equality and inequality restrictions of the safe limit system. From the control parameters, the low voltage drift is improved using the MVPA method. The low-voltage deviation has been exploited for obtaining the maximum capacity of the DG. After that, the maximum capacity is used at maximum location that improves the power flow of the system. The proposed system is performed on MATLAB/Simulink platform, and the effectiveness is assessed by comparing it with various existing processes such as generic algorithm (GA), Cuttle fish algorithm (CFA), adaptive grasshopper optimization algorithm (AGOA) and artificial neural network (ANN).

关键词： Distribution generator optimum location and capacity radial distribution network voltage optimal power flow power loss most valuable player algorithm

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