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International Conference on Measuring Technology and Mechatronics Automation

作者： Hu, Xiaojian Wang, Wei Lu, Jian Southeast Univ Transportat Coll Nanjing Peoples R China

ISBN: (纸本)9780769535838

Traffic accidents usually induce non-recurrent congestions in the urban transportation network. In order to avoid the congestion deterioration, the paper presented a method to make traffic dispersion plans. The method considered and harmonized multi-objective, such as the change in total travel time, restrictions of traffic volumes on the links, and changes in traffic volumes on the links. Firstly, based on satisfactory control theory, the method introduced fuzzy goals and fuzzy constraints into a multi-objective function to obtain an optimal traffic dispersion plan. Secondly, a modified particle swarm optimization algorithm was raised to solve this complex multi-objective problem which has the characteristics of nonlinearity, multi-variable and multi-parameter. Finally, the method was operated on Matlab. The experimental results show that the method is feasibility and validity for accident management to disperse traffic and reduce the impact of the non-recurrent congestion.

关键词： traffic accidents satisfactory control theory multi-objective function modified particle swarm optimization

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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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Fuzzy particle swarm optimization for conjunctive use of groundwater and reclaimed wastewater under uncertainty

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AGRICULTURAL WATER MANAGEMENT 2021年 256卷 107116-107116页

作者： Mirzaie, Nargis Banihabib, Mohammad Ebrahim Shahdany, S. Mehdy Hashemy Randhir, Timothy O. Univ Tehran Coll Aburaihan Dept Water Engn Tehran Iran Univ Massachusetts Coll Nat Sci Dept Environm Conservat Amherst MA 01003 USA

Water scarcity is a significant global challenge. Water resources planning and management are vital to all sectors, primarily for the agricultural sector identified as a significant consumer. Conjunctive water use management is ideal for decreasing water scarcity, especially in arid and semiarid regions. More importantly, considering uncertainties in water management is critical to planning and management issues. This study develops a fuzzy optimization model aiming at a qualitative-quantitative conjunctive reallocation of reclaimed wastewater and groundwater resources. Three objectives, including maximizing economic benefit, minimizing fertilizer consumption, and groundwater withdrawal, were assessed in Iran's Varamin Irrigation Network. This research uses fuzzy and non-fuzzy optimization models for a quantitative-qualitative conjunctive use of reclaimed wastewater and groundwater to meet multi-objective functions. These single and multi-objective fuzzy and non-fuzzy optimization models are compared. The particle swarm optimization (PSO) and multi-objective PSO (MOPSO) algorithms optimized single and multi-objective problems. A fuzzy optimization model is superior to the nonfuzzy optimization model regarding optimizing multi-objective functions. The results indicated that the net benefit increased up to 16% without increasing the cultivated area in the fuzzy MOPSO model. Moreover, groundwater withdrawal was reduced by using a larger share of reclaimed wastewater in total water consumption. This increased the use of potential nitrate in reclaimed wastewater and reduced fertilizer consumption. This study highlights achieving socio-economic and environmental goals under uncertain circumstances using agricultural water strategies through reclaimed wastewater. Also, a general improvement in water consumption efficiency was achieved through the optimal cropping pattern.

关键词： Conjunctive reallocation Environmental sustainability Fuzzy optimization MOPSO algorithm multi-objective function

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Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第10期34卷

作者： Bhagavathi, Hariharan Rathinavelayatham, Siva Shanmugaiah, Kaliraj Kanagaraj, Kamaraj Elangovan, Dinesh SRM Inst Sci & Technol Dept Computat Intelligence Sch Comp Kattankulathur Tamil Nadu India Manipal Acad Higher Educ Manipal Inst Technol Dept Informat & Commun Technol Manipal Karnataka India KPR Inst Engn & Technol Comp Sci & Engn Coimbatore Tamil Nadu India M Kumarasamy Coll Engn Dept Elect & Commun Engn Karur Tamil Nadu India

In cloud computing, virtual machine (VM) consolidation and migration are significant schemes to enhance the energy efficiency and costs of the cloud environment. The process of VM consolidation is to execute a more number of tasks with less power consumption. However, due to unreliable physical resources, energy consumption is increased. So, to solve this issue, improved beetle swarm optimization (IBSO) algorithm based on energy-aware VM consolidation is presented in this article. IBSO algorithm joins together beetle swarm optimization (BSO) and particle swarm optimization (PSO). The proposed algorithm is defined with the effective representation of solution and multi-objective functions such as power consumption, resource wastage, and SLA violation. Results of the work are depicted that the proposed approach outperformed different previous methods in terms of different evaluation measures.

关键词： adaptive beetle swarm optimization and power consumption migration multi-objective function VM consolidation

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OPFaceNet: OPtimized Face Recognition Network for noise and occlusion affected face images using Hyperparameters tuned Convolutional Neural Network

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APPLIED SOFT COMPUTING 2022年第0期117卷 108365-108365页

作者： Lokku, Gurukumar Reddy, G. Harinatha Prasad, M. N. Giri JNT Univ Anantapur Dept Elect & Commun Engn Ananthapuramu Andhra Pradesh India NBKR Inst Sci & Technol Autonomous Dept Elect & Commun Engn Spsr Nellore Andhra Pradesh India

Face recognition is considered as important research in computer vision applications, and it is regarded as the basic biometric security system. Research related to face recognition has been done in the past several years. Still, many more challenges associated with this field need to be addressed. Some literary works have designed the face recognition model on the relatively controlled environments;yet, their performance in general settings has been substandard This paper develops an Optimal Face Recognition Network (OPFaceNet) to recognize the face images affected by high noise and occlusion. The feature patterns subjected to noise like LBP, FLBP, and NRLBP are extracted. The average of all three patterns is given to the proposed Convolutional Neural Network (CNN) classifier. As the main contribution, the CNN model is enhanced by optimizing the Fitness Sorted Rider Optimization Algorithm (FS-ROA). This algorithm optimizes the hyperparameters of CNN like Convolutional layer, Pooling Layer, Fully connected layer, number of Hidden layers, and Type of Pooling. Finally, the simulation results show that the system achieves a good recognition rate of 97.2% and is robust against variations in terms of occlusion and noise when benchmarked over diverse datasets. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Face recognition Occlusion Noise effects Convolutional Neural Network Hyperparameter tuning Fitness sorted rider optimization algorithm multi-objective function

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Optimal DG allocation and sizing in distribution systems with Thevenin based impedance stability index

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 144卷

作者： Gumus, Talha Enes Emiroglu, Selcuk Yalcin, Mehmet Ali Sakarya Univ Elect Elect Engn Dept TR-54050 Sakarya Turkiye

The applications of Distributed Generation (DG) play a significant role to provide the benefit to conventional distribution systems. However, the sizing and location of these DG units should be taken into consideration to get maximum gain and benefit. If DGs are not located and sized properly, the distribution system can be adversely affected. So, optimal allocation and sizing are needed to avoid instability problems and more costs to increase the efficiency and quality of energy in distribution systems. This paper addresses the optimal sizing and allocation of DGs for power losses, voltage profile and stability improvement, introducing a proposed stability index based on Thevenin impedance in a distribution network. The stability index is calculated by the proposed approach based on Thevenin. Thevenin is obtained based on a two-bus system by reduction of more than two bus systems. The optimal sizing and location of DGs are also obtained by operating DGs under different power factors. The reactive capability of DGs is also shown by simulations with DGs operated under different power factors. The problem of optimal sizing and location of DGs, which is a nonlinear optimization problem with discrete and continuous variables, is solved based on the mixed integer Genetic Algorithm (GA). Also, the proposed approach is verified by using Grey Wolf Optimization (GWO) method. The problem contains multi-objectives with three objective functions: system power losses, voltage deviation and the Thevenin based stability index. The proposed approach has been tested on the IEEE-69 and 118-bus test system to demonstrate its effectiveness. The comparison with some other methods shows that the proposed approach gives better results than other methods while reducing the power losses and improving voltage and system stability in all suggested scenarios by finding the optimal placement and sizing of DG units. It is also shown that the reduction in power loss, voltage and stability im-prov

关键词： Distributed generation (DG) Optimal sizing and allocation of DGs multi-objective function Mixed integer optimization Thevenin based stability index

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Power flow management of hybrid system in smart grid requirements using ITSA-MOAT approach

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APPLIED ENERGY 2022年 319卷

作者： Logeswaran, T. Raja, M. Senthil Hameed, Jennathu Beevi Sahul Abdulrahim, Mahabuba Kongu Engn Coll Dept Elect & Elect Engn Erode India Natl Inst Technol Dept Comp Applicat Tiruchirappalli India BS AbdurRahman Crescent Inst Sci & Technol Dept Elect & Elect Engn Chennai India Dubai Mens Coll Higher Coll Technol Dept Elect Engn Dubai U Arab Emirates

A power flow management (PFM) concept of Photovoltaic/Fuel Cell/Battery/Super capacitor in smart grid (SG) system is proposed in this paper. The proposed system controls the photovoltaic (PV) system, battery storage, fuel cell (FC) and super capacitor (SC). The proposed control system is the combination of Improved Tunicate Swarm Optimization (ITSA) and multi objective Artificial Tree (MOAT), hence it is called ITSA-MOAT method. Here, ITSA is building up the control pulses of the inverter using the energy exchange assortment between the source and load side. Here, the composition of multi-objective function is considered according to the obtainable source power and several specified grid produced by the active power and reactive power. To acquire the online control pulses, MOAT is utilized based on the variants of power. Moreover, the global state of charge (SoC) of energy storages and load demand are considered. The ITSA-MOAT method is implemented in MATLAB/Simulink platform. By then, the experimental results of the proposed method are analyzed with the existing methods.

关键词： Power flow management Smart grid Control signals Active power and reactive power varieties multi-objective function

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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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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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PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi-constraint load balancing and routing framework in FANET

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第9期37卷 e5758-e5758页

作者： Ilango, Poonguzhali Sivarajan, Nagarajan Ravichandran, Anitha Arunachalam, Rajesh Panimalar Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India Vellore Inst Technol Dept Micro & Nano Elect Vellore Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai 602105 Tamil Nadu India

Flying ad hoc network (FANET) is a technology that has seen tremendous growth in the past years due to its application in various military and civil developments. Conventional topology-aided routing schemes are not suitable for large-scale FANETs. This is because of the higher mobility rate in UAVs with the architecture differences. Hence, several path entries in routing become invalid and the neighboring nodes can be occupied before the interruption. Hence, it is very important to solve the afore-explained issues in FANET. Initially, the task is assigned to the UAVs. These tasks are performed by satisfying the demands such as timing, congestion, and energy to attain the effective load-balancing performance. Then, the optimal clustering and cluster head (CH) selection are performed using the developed Percentage of Circle Search and Spotted Hyena Optimizer (PCSSHO) in the communication path. Here, the feature selection is done based on constraints like position, speed, moving direction, height variation, link quality, and inter- and intra-cluster distance. Then, the developed PCSSHO model performs routing by considering different constraints such as "end-to-end delay, delivery ratio, power consumption, and link quality." Thus, the recommenced load balancing model in FANET secures an enhanced performance rate than the conventional load balancing frameworks. To implement a multi-constraint load balancing and routing technique that minimizes network congestion and improves the data transmission rate of the FANET technology. To select the optimal CH using the designed PCSSHO approach that supports to derivation of the multi-objective function with certain constraints such as density, eligibility score, inter-cluster distance, and intra-cluster distance. To perform the optimal routing by the suggested PCSSHO scheme in order to formulate the multi-objective function with some of the attributes such as link quality, packet delivery ratio, end-to-end delay, and power consum

关键词： cluster head selection flying ad hoc networks load balancing multi-objective function Percentage of Circle Search and Spotted Hyena Optimizer

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