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A novel multi-objective-derived heuristic-assisted reliable routing path selection scheme in vehicular ad hoc networks

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WIRELESS NETWORKS 2025年第4期31卷 3059-3077页

作者： Devi, S. Kalpana Thenmozhi, R. SRM Inst Sci & Technol Coll Engn & Technol Sch Comp Dept Comp Technol Kattankulathur 603203 Tamil Nadu India

An emerging Mobile Ad-Hoc Network (MANET) is the Vehicular ad-hoc Network (VANET), which is one of the best applications in the transportation system. A VANET should have a huge number of nodes, where the inter-node communication is critical, and it is provided securely, as practical applications solve critical life situations where human lives are at stake in VANETs. It is a decentralized and self-organized system that can be developed using the mobile vehicles, and communication between the vehicles is done by sending traffic-based valuable information. Mobile vehicles act as the source nodes because they are used to transmit and receive traffic-based information on the channel. Altgough the distance of data transmission between the vehicles is limited distance, it can change from one network topology to another using the mobility nature. In multi-hop VANET, message delivery at the destination place is very challenging. The proper path selection among the nodes is very important in VANET. The main hypothesis of the research work is to design a hybridized optimization approach for determining the optimal path between the source and destination vehicle based on the multi-objective functions including distance, link reliability, end-to-end delay, route throughput, Packet Delivery Ratio (PDR), and link duration for lossless packet transmission by reducing the routing overhead in the dynamic vehicular environment. The new hybrid algorithm named Hybrid Tug of War Flow Direction Optimization (HTWFDO) is developed by combining the two well-known algorithms and they are Tug of War Optimization (TWO) and Flow Direction Algorithm (FDA). The MATLAB 2020a simulation tool is utilized to perform the simulation experiment on the developed hybridized optimal path selection process. Based on the experiment, the proposed HTWFDO algorithm provided the throughput value is36.36%, 80%, 36.36%, and 80% increased over the existing algorithms at the node value of *** simulation results

关键词： Vehicular adhoc networks Routing Optimal path selection multi-objective function Hybrid tug of war flow direction optimization Link reliability End-to-end delay Route throughput Packet delivery ratio Link duration

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An innovative energy efficient routing protocol in MANET with hybridized osprey-fire hawk optimization algorithm to attain optimal routing constraints

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WIRELESS NETWORKS 2025年第3期31卷 2005-2026页

作者： Saravanan, N. Arunachalam, Rajesh Nisha, A. Sahaya Anselin Karthikayen, A. KSR Coll Engn Dept CSE IoT Tiruchengode 637215 Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai 602105 Tamil Nadu India Sathyabama Inst Sci & Technol Dept Elect & Commun Engn Chennai 600119 Tamil Nadu India

A quickly deployable wireless, self-organizing, infrastructure-free network is Mobile Adhoc Networks (MANETs), they are particularly well suited for providing flawless communications during military surgeries, natural disasters, and crises without a radio infrastructure. Due to the changing dynamic topology and flexibility of the structures, security is the major concern in MANET. It is vulnerable to various attacks, including program modification, routing, and eavesdropping. When compared to Quality of Service (QoS) problems, the security problems on MANET are worse. Therefore, the best method to guarantee the security of MANET is intrusion tracking, it handles the network by recognizing violations. The efficacy and security of the MANET is increased by the intrusion detection approaches. The capacity of the cellular node to send packets is based on the overall system life, and it might also be altered by the power collapse of the node. For better navigation in MANET, the multi-paths and the routing protocol should be chosen carefully. Because of the constrained resources and changing dynamic topology, performing safe energy-efficient routing is difficult in MANET. Thus, it is highly crucial to resolve several challenges presented in the traditional routing techniques. So, a new routing technique for MANET is designed by considering the optimal routing constraints. Here, a new Hybridized Osprey and Fire Hawk Optimization Strategy (HO-FHOS) is employed to enhance the energy-efficient features in the routing process. The initiated routing protocol considers the route length in selecting the best route. Moreover, the optimal routing constraints such as distance, end-to-end delay, residual energy, link reliability, Packet Delivery Ratio, path loss, throughput, and route survival rate are achieved using the developed model (HO-FHOS). Hence, the implemented energy-efficient routing protocol secured a superior performance rate than the conventional energy-efficient routin

关键词： Mobile Adhoc Networks Energy efficient routing Hybridized osprey and fire hawk optimization strategy Optimal path selection multi-objective function

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multi-objective Evaluation Method for Efficient Water Energy Utilization in multistage Hydraulic Turbines With Ultrahigh Water Head and Low Flow Rate

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ENERGY SCIENCE & ENGINEERING 2025年第1期13卷 434-451页

作者： Zhang, Zichao Zhao, Sanya Li, Yanpin North China Univ Water Resources & Elect Power Engn Training Ctr Zhengzhou Henan Peoples R China North China Univ Water Resources & Elect Power Henan Fluid Machinery Engn Technol Res Ctr Zhengzhou Henan Peoples R China North China Univ Water Resources & Elect Power Coll Energy & Power Engn Zhengzhou Henan Peoples R China

In this article, a multi-objective comprehensive evaluation method is established by comprehensively considering the power and shaft diameter of a multistage hydraulic turbine with an ultrahigh water head and low flow rate to utilize water energy efficiently. Using this method, several schemes for calculating the runner's geometric parameters are attained through the scheme design of different maximum numbers of stages and rotational speeds under different operating conditions of water pressure and flow rate. The reasonable schemes are determined by the maximum value in the intersection of runner diameter value ranges, the blade inlet angle beta 1 >= 12 degrees and the blade inlet flow angle alpha 1 >= 6 degrees. Based on the multi-objective function of water energy utilization considering the comprehensive performance of the runner diameter and power, the design parameters and design stage numbers of the multistage hydraulic turbine with the optimal comprehensive performance of power and shaft diameter are obtained. This method is recommended for the design of ultra-low specific speed multistage hydraulic turbines with a specific speed of less than 50.

关键词： design stage number evaluation method for water-energy-efficient utilization multi-objective function multistage hydraulic turbine runner parameters

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A meta-heuristic approach-aided multi-objective strategy with optimal resource allocation via fault tolerant and priority-based scheduling for load balancing in cloud

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WIRELESS NETWORKS 2025年第3期31卷 2419-2441页

作者： Lokesh, Gudivada Baseer, K. K. Mohan Babu Univ Dept Comp Sci & Engn Tirupati 517102 Andhra Pradesh India VEMU Inst Technol Dept CSE Chittoor 517112 Andhra Pradesh India GITAM GITAM Sch Technol Dept Data Sci Bengaluru 560001 Karnataka India

Cloud computing has been referred to as a successive high-functionality computing sector in the past years for organizations and also individuals. Numerous major previous experiments on the cloud computing sector have existed but there are still specific problems with the distribution of workload in the system of cloud networks. Effective task allocation is complex due to the existence of various Virtual Machines (VMs) and resources. In these approaches, the CSPs must ensure better service delivery functionality, preventing complexities like under or overloaded hosts. These may increase the processing period or result in machine damage. The primary concept of the recommended mechanism is to develop load balancing in the cloud computing sector with optimal resource allocation. With the quick network utilization and also the traffic of data in the cloud sector, the intensity of the computation and the power of processing are improved. In order to decrease the pressure of the network and to enrich the efficacy of the computation the load balancing mechanism is significant. In the starting phase, the scheduling process is executed with the utilization of fault tolerance and a priority-based scheduling process. Then, the optimization of the resources in the scheduling is done with the Modified Random Parameter Dragonfly Algorithm. To further evaluate the load balancing model, the objective function is derived by makespan, average resource utilization, throughput, delay, success rate, and execution time. On the other hand, owing to the dynamic nature of the model, the server (resource) status is varied continuously. Thus, in order to assign the task to the VM resources or server and to efficiently balance the load in the cloud model, the current status of the server is predicted by the Attention-based Bilateral Long Short-term Term Memory (ABi-LSTM) model before resource allocation. From the result analysis, the throughput of the developed model is 95.5, and also the thro

关键词： Cloud computing Scheduling and resource optimization multi-objective function Load balancing Modified random parameter dragonfly algorithm Attention based bilateral long short term memory

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Optimizing multi-Product Pipeline Network Configuration Design: A Comprehensive Framework With objective function Sensitivity Analysis

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IEEE ACCESS 2024年 12卷 18804-18820页

作者： Sharma, Bharat Khare, Sunil Kumar Univ Petr & Energy Studies UPES Dept Petr & Earth Sci Dehra Dun 248007 Uttarakhand India HPCL Pipeline Dept Mumbai 400001 Maharashtra India

This work presents a novel framework for the design optimization of Pipeline Network Configuration which takes into account the intricate relationships among decision variables and the essential technical, commercial, and environmental design parameters. To illustrate the Single Horizon Optimization methodology's efficacy and sensitivity analysis of different objective functions, a case study of the China multi-product Pipeline is utilized. It is found that each performance parameter excels in its respective objective function, however, a novel Tariff & Carbon-Emissions multi-objective function stands out by delivering a well-balanced outcome, reducing overall cost, tariff, and carbon emissions by -1.98%, -1.76%, and -3.48%, respectively. The article also delves into various multi-Horizon Optimization (MHO) methodologies aligning with practical industry practices, and the case study of the Central India Pipeline is used. The Pumping-Station MHO shows a potential cost savings of 1.21 Billion INR to the pipeline owner, along with a reduction of tariffs by 139 INR/MT across the analysis period. The Pipeline-Laying MHO analysis reveals that the initial phasing for the PNC design shall be up to the year when demand reaches 80% of the total demand. The proposed framework empowers designers with better decision-making capabilities for economically viable and environmentally sustainable pipeline network design.

关键词： Energy multi-horizon optimization multi-objective function NSGA-II petroleum pipeline network design tariff technical-commercial-environmental

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Application of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) in a Two-Echelon Cold Supply Chain

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SYSTEMS 2025年第3期13卷 206-206页

作者： Acerce, Asli Denizhan, Berrin Sakarya Univ Engn Fac Dept Ind Engn TR-54050 Sakarya Turkiye

A two-stage cold supply chain manages the transportation, storage, and distribution of temperature-sensitive products like frozen food, fresh/green products, and pharmaceuticals, which makes it costly. It consists of three key elements: a supplier, a warehouse, and multiple customers. Procurement planning can be conducted for various products, and this study assumes the transport of a fresh/green product with gradually decreasing quality due to its perishable nature. In a two-stage cold supply chain, multiple objective functions can be defined, including cost minimization, product quality optimization, and transportation/storage condition optimization. We developed a mathematical model to optimize these objectives, incorporating two specific functions, cost minimization and product age reduction, to ensure efficient supply chain performance. Traditional solution methods often struggle with multi-objective mathematical models due to their complexity. Therefore, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), a Genetic Algorithm-based approach, was applied to solve the model efficiently. NSGA-II optimized planning for a 7-day period under specific demand conditions, ensuring better resource allocation. The results showed that NSGA-II was better than traditional methods at making decisions and routing efficiently in the two-stage cold supply chain. This led to much better outcomes, with lower costs, less waste, and better product quality throughout the process.

关键词： two-echelon inventory system cold supply chain multi-objective function meta-heuristic algorithm NSGA-II

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Portfolio optimization with MOPSO-Shrinkage hybrid model

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RESULTS IN CONTROL AND OPTIMIZATION 2025年 19卷

作者： Tran, Minh Nguyen, Nhat M. Ho Chi Minh Univ Banking 36 Ton That Dam StrNguyenThai Binh WardDist 1 Ho Chi Minh 700000 Vietnam

This paper introduces a novel framework for portfolio optimization that integrates multi objective Particle Swarm Optimization (MOPSO) with shrinkage covariance estimators, referred to as the MOPSO-Shrinkage hybrid model. The main contribution of this study lies in combining the adaptive search capabilities of evolutionary algorithms with robust covariance estimation techniques to enhance portfolio allocation in mature financial markets. Unlike traditional shrinkage covariance models, which struggle in highly dynamic environments, our hybrid model optimally selects stocks and improves risk-adjusted returns. Empirical analysis on stock market data from 2013 to 2023 demonstrates that MOPSO-Shrinkage models consistently outperform traditional shrinkage models, achieving higher returns, lower volatility, and superior Sharpe ratios. Among the hybrid models, MOPSO-SSIM exhibits the best performance, an average annual return of 18.86% and a Sharpe ratio of 1.27, while significantly reducing portfolio risk. Rigorous statistical tests confirm the robustness of the model, showing MOPSO-Shrinkage significantly outperforms traditional methods. These findings suggest that proposed approach is well-suited for traders seeking higher risk-adjusted returns and portfolio stability in volatile markets.

关键词： multi-objective function Particle swarm optimization Shrinkage estimator Stock selection Portfolio allocation

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An Inverse FEM for Structural Health Monitoring of a Containership: Sensor Network Optimization for Accurate Displacement, Strain, and Internal Force Reconstruction

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SENSORS 2025年第1期25卷 276-276页

作者： Bardiani, Jacopo Oppezzo, Christian Manes, Andrea Sbarufatti, Claudio Politecn Milan Dept Mech Engn Via G La Masa 1 I-20156 Milan Italy

In naval engineering, particular attention has been given to containerships, as these structures are constantly exposed to potential damage during service hours and since they are essential for large-scale transportation. To assess the structural integrity of these ships and to ensure the safety of the crew and the cargo being transported, it is essential to adopt structural health monitoring (SHM) strategies that enable real-time evaluations of a ship's status. To achieve this, this paper introduces an advancement in the field of smart sensing and SHM that improves ship monitoring and diagnostic capabilities. This is accomplished by a framework that combines the inverse finite element method (iFEM) with the definition of an optimal Fiber Bragg Gratings-based sensor network for the reconstruction of the full field of displacement;strain;and finally, cross-section internal forces. The optimization of the sensor network was performed by defining a multi-objective function that simultaneously considers the accuracy of the displacement field reconstruction and the associated cost of the sensor network. The framework was successfully applied to a mid-portion of a containership case, demonstrating its effective applicability in real and complex scenarios.

关键词： inverse finite element method internal force reconstruction containerships multi-objective function structural health monitoring

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Distributed Generators Optimization Based on multi-objective functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

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ENERGIES 2020年第15期13卷 3847-3847页

作者： Hemeida, Mahmoud G. Alkhalaf, Salem Mohamed, Al-Attar A. Ibrahim, Abdalla Ahmed Senjyu, Tomonobu Minia Higher Inst Engn Dept Elect Engn Al Minya 61111 Egypt Qassim Univ Dept Comp Sci Arrass Coll Sci & Arts Qasim 51431 Saudi Arabia Aswan Univ Dept Elect Engn Fac Engn Aswan 81511 Egypt Univ Ryukyus Fac Engn 1 Senbaru Nakagami 90370213 Japan

Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Levy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (epsilon constraint)), multi-objective Harris Hawks Optimization (MOHHO), multi-objective Improved Harris Hawks Optimization (MOIHHO), multi-objective Particle Swarm Optimization (MOPSO), and multi-objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved;and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

关键词： optimization techniques manta ray foraging optimization algorithm multi-objective function radial networks optimal power flow

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On quasi-orderings and multi-objective functions

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期177卷 1591-1598页

作者： Kaminski, B. Warsaw Sch Econ Decis Anal & Support Dept PL-02554 Warsaw Poland

In the paper the relationship between quasi-orderings (i.e. transitive and reflexive relations) and multi-objective functions (i.e. functions being a finite vector of continuous value functions) is investigated. A characterization of quasi-orderings representable by multi-objective functions is given. Within the introduced theoretical framework it is, in particular, shown that preferences implied by a first order stochastic dominance relation cannot be represented by multi-objective functions. (c) 2005 Elsevier B.V. All rights reserved.

关键词： multi-objective function quasi-ordering preference representation uncertainty modeling

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