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An Hybrid Heuristic optimal relay selection strategy for energy efficient multi hop cooperative cellular communication

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AD HOC NETWORKS 2023年 140卷

作者： Kiran, Uppula Kumar, Krishan Lovely Profess Univ Dept Elect & Commun Engn Phagwara 144001 Punjab India Lovely Profess Univ Fac Sch Elect & Elect Engn Phagwara 144001 Punjab India

One of the major constraints for mobile relay networks is energy-efficient communication owing to the restricted battery power of user terminals. Among them, the production method is cooperative cellular networks that focus on enhancing the data rate, network reliability, and network system capacity and also improving the spectrum resources in the Fifth Generation (5 G) networks. Thus, recent research works investigate the selection of mobile relays in vehicular networks and also the relay selection in cooperative communication. Here, the mobile relay is selected by the base station (operator) for every mobile user for reducing the cost with improved data rates and reducing the total transmission power. The selection of relay is performed by considering the benefits or utilities offered to three players such as mobile relay, mobile user, and operator. Conventional relay node selection approaches face challenges regarding reduced network lifetime, delay, increased energy consumption, and collusion attacks. For next-generation wireless networks, both energy efficiency and capacity are vital. To cope with these problems, a new multi-objective derived energy efficient accurate selection of relay in multi-hop cooperative cellular communication in the vehicular network through a hybrid heuristic strategy is imple-mented. The conventional approaches are utilized for probability-based multi-hop broadcasting for low latency. On the other hand, the major demerits of cellular communication are developing an effective multi-hop communication strategy that generates hidden node problems, and also it decreases the speed of the vehicles and the lack of infrastructure in network topology. Thus, this paper aims to propose a hybrid optimization model to choose the lowest count of multi-hops among the source and destination for the cooperative cellular vehicular communication network. The major issue well thought-out here is accurately choosing the count of hops or relays for appropri

关键词： multi-hop cooperative cellular communication Vehicular network Energy efficient optimal relay selection Best fitness-derived crow tunicate swarm opti-mization algorithm multi-objective function Link reliability

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multi-objective-derived energy efficient routing in wireless sensor network using adaptive black hole-tuna swarm optimization strategy

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AD HOC NETWORKS 2023年 144卷

作者： Sheeja, R. Iqbal, M. Mohamed Sivasankar, C. Easwari Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India VIT AP Univ SCOPE Amaravati Andhra Pradesh India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India

Wireless Sensor Network (WSN) has a significant factor in energy conservation as it is effectively impracticable for recharging the energy of the nodes in their remote location. Here, WSN is a compilation of several sensor nodes that are statically self-organized into a wireless network without utilizing any previous infrastructure. The major issues in WSNs are consuming more energy and decreasing network lifetime. On the other hand, the nodes utilize huge amounts of energy while forwarding the data to the Base Station (BS). However, the network duration in WSN is challenging regarding the restricted energy of sensor nodes. Thus, an effective routing protocol in this research work is utilized for data forwarding to reduce energy consumption and also increase the network lifetime. The main intent of this research is to explore a novel energy-saving routing with the integration of a newly suggested heuristic algorithm termed Adaptive Black hole Tuna Swarm Optimization (ABTSO) with the integration of the Black Hole algorithm (BHO) and Tuna Swarm Optimization (TSO) techniques. This efficient routing in WSN is performed via adopting the multi-functional derivation owing to the factors like restart number, link quality, node centrality, node degree, path loss, Packet Drop Ratio (PDR), delay, the distance among the Cluster Head (CH) and BS, the distance between the sensor nodes, and residual energy of the CH. This multi-objective derived CH selection helps to maximize network throughput and network lifetime. Thus, the experimental outcome of the developed ABTSO method attains 1.2%, 1.4%, 0.6%, and 1.4% superior to BOA, SHO, BHO, and TSO regarding normalized energy. Thus, the developed ABTSO method achieves better performance in terms of delay, network lifetime, and packet loss.

关键词： Wireless sensor network Cluster head selection Adaptive black hole-tuna swarm optimization strategy multi-objective function

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Analytical study on optimized configuration strategy of electrochemical energy storage system under multiple scenarios

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Li, Tou Zhang, Wei Zhang, Xuan School of Electrical Engineering Nanjing Institute of Technology Jiangsu Nanjing210000 China Nanjing Weilixuan Information Technology Co. Ltd Jiangsu Nanjing210000 China

This paper models the electrochemical energy storage system and proposes a control method for three aspects, such as battery life, to generate a multiobjective function for optimizing the capacity allocation of electrochemical energy storage under multiple scenarios, with conditional constraints on the system, storage, and progression aspects. The improved whale optimization algorithm is used to solve the multi-objective function to find the most reasonable electrochemical energy storage system capacity optimization allocation scheme. Using the model constructed in this paper under multi-scenario conditions, it is found after solving that the optimal allocation scheme purchases power from the grid at around 25MW during the highest peak hours in summer and 5MW in winter, which ensures the economic benefits. Meanwhile, the maximum power fluctuation of the electrochemical energy storage system at point A of the optimization strategy provided by the model is only 2.16%, which is much lower than the preset 4.32%, so the optimal allocation strategy reaches the optimum. Comparing the performance of configured energy storage in different scenarios, the peak-valley power difference of the model proposed in this paper decreases from 11.6 MW to 8.9 MW, which is a better performance than that of the control group, which is 10.8 MW-9.1 MW, and the effect of peak shaving and valley filling is obvious. © 2024 Tou Li, Wei Zhang and Xuan Zhang, published by Sciendo.

关键词： Electrochemical energy storage multi-objective function Optimal allocation Whale optimization algorithm

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Minimization of Operational and Seepage Losses in Agricultural Water Distribution Systems Using the Ant Colony Optimization

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WATER RESOURCES MANAGEMENT 2021年第3期35卷 827-846页

作者： Azargashb Lord, Sara Hashemy Shahdany, Seied Mehdy Roozbahani, Abbas Univ Tehran Dept Water Engn Coll Aburaihan Tehran Iran

The present study's main objective is to simultaneously minimize operational and seepage losses in agricultural water distribution systems, relying on the Ant Colony Optimization (ACO). For this purpose, the following arrangements were made: i) Hydraulic flow simulation of the distribution systems was conducted by developing an Integrator Delay (ID) model using MATLAB and appraisal performance of the water distribution system, ii) The seepage simulation alongside the system employing a calibrated and validated estimation equation, iii) developing the ACO model to minimize operational and seepage losses within the agricultural water distribution Systems. Two single-objective and one multi-objective functions were considered to minimize seepage loss, operational loss, and both loss components simultaneously. The Moghan irrigation water distribution system, Iran, was selected as the case study. Optimization results revealed that the first through third objective functions managed to reduce the total losses in the Moghan water distribution systems by, respectively, 0.39, 3.1, and 4% compared to the existing conditions. A comparison with the optimization results from LINGO, a nonlinear optimization model, was suggestive of the advantages of the ACO in terms of the optimal result and optimization time. The proposed method can be used as a practical measure to improve water productivity within the scale of agricultural water distribution systems by improving the manual operating system's performance in the status quo.

关键词： Seepage modeling Hydraulic flow simulation Losses minimization multi-objective function Ant Colony algorithm

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Energy and spectral efficiency improvement using improved shark smell-coyote optimization for massive MIMO system

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第2期36卷 e5381-e5381页

作者： Byreddy, Amarender Reddy Logashanmugam, E. Sathyabama Inst Sci & Technol Elect & Commun Engn Chennai India

Massive multiple-input multiple-output (MIMO) can considerably enhance the "spectral efficiency and energy efficiency" since it is a major technique for future wireless networks. Thus, the performance needs a huge count of base station antennas to serve a smaller number of terminals in conventional MIMO methodology. Large-scale radio frequency (RF) chains represent the large-scale antennas. There is a need of implementing an effective massive MIMO system for maximizing the efficient performance of the system with high "spectral efficiency and energy efficiency" owing to the high cost of RF chains, and the higher power consumption. In this paper, a massive MIMO communication system is implemented to satisfy the requirements regarding "energy efficiency and spectral efficiency." Here, the number of base station antennas, the transmit power, and beam forming vectors are optimized to maximize "energy efficiency and spectral efficiency" when the channel capacity is known to be higher than some threshold values. The novelty of this work is a new hybrid optimization adaptive shark smell-coyote optimization (ASS-CO) algorithm is developed for improving energy efficiency. The optimization is done with the help of the hybrid optimization ASS-CO Algorithm. The proposed ASS-CO algorithm-based massive MIMO communication system is evaluated by experimental analysis. From the result analysis, the maximum resource efficiency is observed by SS-WOA, which is 6.6%, 50%, 6.6%, 6.6%, and 6.6% maximized than rider optimization algorithm (ROA), spotted hyena optimization (SHO), lion algorithm (LA), Shark Smell Optimization (SSO), and Coyote Optimization Algorithm (COA) by taking the count of base stations as 4. The superior performance enhancement regarding "spectral efficiency and energy efficiency" is accomplished over the traditional systems.

关键词： "spectral efficiency and energy efficiency" enhancement adaptive shark smell-coyote optimization algorithm hybrid meta-heuristic learning massive multiple-input multiple-output system multi-objective function

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Techno-Economic Strategy for the Load Dispatch and Power Flow in Power Grids Using Peafowl Optimization Algorithm

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ENERGIES 2023年第2期16卷 846-846页

作者： Ali, Mohammed Hamouda El-Rifaie, Ali M. Youssef, Ahmed A. F. Tulsky, Vladimir N. Tolba, Mohamed A. Al Azhar Univ Fac Engn Dept Elect Engn Cairo 11651 Egypt Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait Natl Res Univ MPEI Elect Power Syst Dept Moscow 111250 Russia Egyptian Atom Energy Author EAEA Nucl Res Ctr Reactors Dept Cairo 11787 Egypt

The purpose of this paper is to address an urgent operational issue referring to optimal power flow (OPF), which is associated with a number of technical and financial aspects relating to issues of environmental concern. In the last few decades, OPF has become one of the most significant issues in nonlinear optimization research. OPF generally improves the performance of electric power distribution, transmission, and production within the constraints of the control system. It is the purpose of an OPF to determine the most optimal way to run a power system. For the power system, OPFs can be created with a variety of financial and technical objectives. Based on these findings, this paper proposes the peafowl optimization algorithm (POA). A powerful meta-heuristic optimization algorithm inspired by collective foraging activities among peafowl swarms. By balancing local exploitation with worldwide exploration, the OPF is able to strike a balance between exploration and exploitation. In order to solve optimization problems involving OPF, using the standard IEEE 14-bus and 57-bus electrical network, a POA has been employed to find the optimal values of the control variables. Further, there are five study cases, namely, reducing fuel costs, real energy losses, voltage skew, fuel cost as well as reducing energy loss and voltage skew, and reducing fuel costs as well as reducing energy loss and voltage deviation, as well as reducing emissions costs. The use of these cases facilitates a fair and comprehensive evaluation of the superiority and effectiveness of POA in comparison with the coot optimization algorithm (COOT), golden jackal optimization algorithm (GJO), heap-based optimizer (HPO), leader slime mold algorithm (LSMA), reptile search algorithm (RSA), sand cat optimization algorithm (SCSO), and the skills optimization algorithm (SOA). Based on simulations, POA has been demonstrated to outperform its rivals, including COOT, GJO, HPO, LSMA, RSA, SCSO, and SOA. In addition

关键词： optimal power flow peafowl optimization algorithm control variables multi-objective function load dispatch

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Performance analysis of solar PV parameter estimation techniques

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OPTIK 2023年 279卷

作者： Rawat, Nikita Thakur, Padmanabh Singh, Asheesh K. Bansal, R. C. G Era Deemed be Univ Dept Elect Engn Dehra Dun 248002 Uttaranchal India Motilal Nehru Natl Inst Technol Allahabad Dept Elect Engn Prayagraj 211004 Uttar Pradesh India Univ Sharjah Coll Engn Dept Elect Engn Sharjah U Arab Emirates Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa

Firstly, this paper investigates the performances of the three different established parameter estimation techniques, namely multi-objective function, five-point metaheuristic, and hybrid-based approaches. Using these three different techniques, unknown model parameters, such as photovoltaic current (I-pv), dark saturation current (I-o), series resistance (R-s), shunt resistance (R-sh), and Ideality factor (A), of mono-and poly-crystalline technology-based solar PV modules, are predicted. Afterwards, using the estimated unknown parameters and P-V & I-V characteristics curves, two different benchmarks, namely absolute relative maximum power error (ARMPE) and overall model error (OME), of these modules, are evaluated to reveal their best performances. In addition, various other parameter estimation techniques, such as iterative, analytical, curve-fitting, and metaheuristics-based methods, have also been considered for performance analysis. It is shown that the hybrid-based approach provides a better characterization than the other various established approaches. The value of %ARMPE is found almost zero with the hybrid approach for both modules. In addition, the smallest values of OME are obtained with the hybrid approach, i.e., 0.002 or 0.003 for poly- and 0.031 for the mono-crystalline technology-based solar PV module. Hence, a hybrid-based parameter estimation technique is suggested to achieve accurate characteristics of solar PV.

关键词： Absolute relative maximum power error Hybrid approach Iterative technique multi-objective function Overall model error Photovoltaic cell

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multi-objective-derived energy efficient routing in wireless sensor networks using hybrid African vultures-cuckoo search optimization

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第6期36卷 e5438-e5438页

作者： Asha, A. Rajesh, A. Verma, Neha Poonguzhali, I Rajalakshmi Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India SIMATS Saveetha Sch Engn Dept Elect & Commun Engn Chennai Tamil Nadu India GGSIPU Vivekananda Inst Profess Studies Dept Informat Technol New Delhi India Panimalar Inst Technol Dept Elect & Commun Engn Poonamallee Tamil Nadu India

Wireless Sensor Network (WSN) plays an essential role in consumer electronics, remote monitoring, an electromagnetic signal, and so forth. The functional capacity of WSN gets enhanced everyday with different technologies. The rapid development of wireless communication, as well as digital electronics, provides automatic sensor networks with low cost and power in various functions, but the challenge faced in WSN is to forward a huge amount of data between the nodes, which is a highly complex task to provide superior delay and energy loss. To overcome these issues, the development of a routing protocol is used for the optimal selection of multipath to perform efficient routing in WSN. This paper developed an energy-efficient routing in WSNs utilizing the hybrid meta-heuristic algorithm with the help of Hybrid African Vultures-Cuckoo Search Optimization (HAV-CSO). Here, the designed method is utilized for choosing the optimal cluster heads for progressing the routing. The developed HAV-CSO method is used to enhance the network lifetime in WSN. Hence, the hybrid algorithm also helps select the cluster heads by solving the multi-objective function in terms of distance, intra-cluster distance, delay, inter-cluster distance, throughput, path loss, energy, transmission load, temperature, and fault tolerance. The developed model achieved 7.8% higher than C-SSA, 25.45% better than BSO-MTLBO, 23.21% enhanced than AVOA, and 1.29% improved than CSO. The performance of the suggested model is validated, and the efficacy of the developed work is proved over other existing works.

关键词： cluster head selection energy-efficient routing hybrid African vultures-cuckoo search optimization multi-objective function wireless sensor network

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Optimal Planning of Distributed Generation Using Improved Grey Wolf Optimizer and Combined Power loss Sensitivity

Optimal Planning of Distributed Generation Using Improved Gr...

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IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Sodani, Mohamed Aly, Hamed H. Little, Timothy A. Dalhousie Univ Elect & Comp Engn Halifax NS Canada

ISBN: (纸本)9781665448642

This paper introduces a hybrid method for finding the best location and size of distribution generation (DG) sources in a distribution system. The strategy employs Combined Power Loss Sensitivity (CPLS) and the algorithm Improved Grey Wolf Optimizer (I-GWO), with CPLS determining candidate locations for DG, and I-GWO determining the best location and size based on CPLS suggestions for candidate buses. The overall aim of this approach is to improve system stability, enhance voltage profile, and minimize power loss. The work evaluates the novel strategy using IEEE-33 and IEEE-69 bus radial distribution systems and investigates three kinds of DG to make comparisons of key efficiency and performance metrics. The test results show that, in comparison to Other optimization methods, the proposed hybrid approach with multi-objective functions offers optimal results.

关键词： Distributed Generation Optimization Improved Grey Wolf Optimizer multi-objective function

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A new methodology for secured inventory management by average fitness-based colliding bodies optimization integrated with block chain under cloud

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第1期34卷

作者： Chinnaraj, Govindasamy Antonidoss, Arokiasamy Hindustan Inst Technol & Sci Dept Comp Sci & Engn Chennai Tamil Nadu India

This article tries to develop the intelligent secured inventory management strategy by blockchain technology under cloud sector. multiple suppliers, a manufacturer and multiple distributors are considered in the proposed model. The proposed inventory management is derived as a multi-objective model, which is solved by the average fitness-based colliding bodies optimization. A set of cost parameters like "transaction cost, inventory holding cost, shortage cost, transportation cost, time cost, setup cost, backordering cost, quality improvement cost, procurement cost, and stock-out cost" is used for deriving the multi-objective function. The optimal solution with the reduced cost function is stored in the block chain under cloud sector by each distributor, where a hash function is used by the distributor to secure the data that could not be visible to any other distributors. The proposed methodology aims to provide an important tool for the management of proposed echelon tree structured supply chains with block chain security under cloud sector that overcomes some of the deficiencies of competing methodologies.

关键词： average fitness-based colliding bodies optimization block chain technology cloud environment distributor manufacturer multi-objective function secured inventory management supplier

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