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36th ASME International Conference on Ocean, Offshore and Arctic Engineering (OMAE) / Symposium on Safety of Marine Structures and Operations in Honor of Torgeir Moan

作者： Mao Xiaofei Zhang Wenxu Qian Jiankui Wu Minghao WUT Sch Transportat Minist Educ Key Lab High Performance Ship Technol Wuhan Hubei Peoples R China WUT Sch Transportat Wuhan Hubei Peoples R China

ISBN: (纸本)9780791857632

This paper focuses on the application of a ship hull form multi-disciplinary optimization (MDO) system based on the computational fluid dynamics (CFD). Using the iSIGHT software, the MIO system integrates an automatic geometry transformation program and high-fidelity CFD solvers for different sub-disciplines. Hydrodynamics analysis subsystem includes resistance, seakeeping and stability modules. The resistance and seakeeping is analyzed by commercial potential flow CFD codes, the stability is assessed by in-house code. The geometry variation output can be automatically used by the numerical solvers. By means of the design of experiment (DOE) technique, a neural network metamodel is trained to predict short term motion response of the derived ships efficiently. The system has been used in a seismic vessel's hull form optimization to minimize the resistance and maximize the long term seakeeping operability index. Meanwhile, the stability in waves is concerned as a constraint. The hybrid MIGA-NLPQL optimization algorithm is applied for a global-to-local search in resistance optimization. For the synthesis optimization, a Pareto optimal solution set has been obtained and the final solution is achieved by trade-off analysis of the solution set. The entire automatic optimization process can be used for the preliminary design of new high performance vessels.

关键词： Multi-disciplinary optimization Seakeeping neural network meta-model hybrid optimization algorithm iSIGHT CFD

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Optimizing Resource Utilization in IoT based Smart Home Automation: A hybrid Crow Search and Border Collie optimization Approach

Optimizing Resource Utilization in IoT based Smart Home Auto...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Dhivya, V.P. Adhiyaman, S.V. Elangavi, S. Gokul, R. Department of Information Technology Tiruchengode637 215 India

ISBN: (纸本)9798350359688

Smart home automation systems are now essential parts of contemporary living, providing convenience, energy efficiency, and improved security. Efficient compute offloading solutions are crucial for achieving optimal performance and resource utilisation in these systems. This research presents a new method for improving the efficiency of resource usage in smart home automation. The method utilises a hybrid algorithm that combines the Crow Search algorithm (CSA) with the Border Collie Optimisation algorithm (BCOA). The hybrid method is designed to tackle the difficulties of computing offloading by combining the advantageous aspects of the collective foraging behaviour of crows and the intellect of border collies. We introduce the hybrid algorithm and describe its design, implementation, and assessment through simulation-based experiments. The results indicate that the proposed approach effectively optimises resource utilisation, enhances system efficiency, and improves overall performance in smart home automation scenarios. This research enhances the field of smart home automation by proposing a new optimisation technique specifically designed to meet the specific needs and limitations of these systems. © 2024 IEEE.

关键词： Border Collie optimization algorithm Computation Offloading Crow Search algorithm hybrid optimization algorithm Resource Utilization

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WSNetDefender: Securing Wireless Sensor Networks using BBIDNet and Fuzzy-DQN Threat Mitigation System (FD-TMS)

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IEEE Access 2025年 13卷 93439-93452页

作者： Aleisa, Mohammed A. Majmaah University College of Computer and Information Sciences Department of Computer Science Al-Majmaah 11952 Saudi Arabia

In the Wireless Sensor Network (WSN), there has been a significant increase in intruders, owing towards rapid expansion in the cyber-space that encloses multi-layered cybersecurity. The aim of this work is to tackle the increasing cybersecurity issues in Wireless Sensor Networks (WSNs) by proposing an innovative framework named WSNetDefender. It encompasses a Boosted BiLSTM Intrusion Detector Network (BBIDNet) to identify intrusions with precise accuracy and a Fuzzy-DQN Threat Mitigation System (FD-TMS) to respond to threats dynamically. The framework aims to enhance the accuracy of detection of intrusion while reducing false positives and resource *** first step involves gathering data from two distinct wireless sensor network datasets. Subsequently, preprocessing is done using the new Integrated Preprocessing Engine (IPE), wherein the Gaussian filtering for denoising, Kalman filtering for data fusion (2 databases), and Min-Max normalization for standardization are available. Then, features are extracted using the new NetFlow Profiling Network (NPN), which encloses the CAN Bus Analysis, Behavior Profiling, and Netflow Analysis. The newly introduced Jackal-Wolf hybrid Optimizer (JWHO) assisted in selecting the optimal features from NPN, thereby assisting the model in lowering computing difficulty in terms of both memory and time. The proposed JWHO is the conceptual synergy of Golden Jackal optimization and Grey Wolf Optimizer (GJO-GWO), respectively. The extracted JWHO-based features are utilized to train the new BBIDNet, which makes the accurate detection of intruders in WSN. This BBIDNet is the combination of the Bidirectional Long Short-Term Memory (BiLSTM) network with AdaBoost. Once, an attacker is found to be available, then for the concern attacker is mitigated via the new FD-TMS. In FD-TMS, the threat scoring and prioritization are undergone with respect to fuzzy rules, and based on the critical level identified, the mitigation is done using the Dee

关键词： Cybersecurity Deep Learning hybrid optimization algorithm Intrusion Detection Reinforced Learning Threat Mitigation System WSN

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A New optimization Approach Based on Firefly and Cuckoo Search algorithm for Dynamic Multi-Objective optimization Problem in Workflow Scheduling

A New Optimization Approach Based on Firefly and Cuckoo Sear...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Naga Raju, M. Patra, Raj Kumar Nagesh, D. Kulkarni, Madhusudan Devi, R. Meena Cse GST Gitam Bengaluru India Cmr Technical Campus Department of Computer Science and Engineering Hyderabad India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India Tontadarya College of Engineering Gadag Department of Ece India Agni College of Technology Cse Chennai India

ISBN: (纸本)9798350313413

Workflow scheduling is a well-explored topic for research in cloud computing, that focuses on maximizing the usage of cloud resources for different workflow activities. In this research, the topic of dynamic workflow scheduling is considered by addressing it as a Dynamic Multi-Objective optimization Problem (DMOP). The dynamism in this challenge originates from two key sources: resource failures and the possible fluctuation in the number of goals. Resource failures may arise from software or hardware problems, producing the first form of dynamism. Furthermore, real-world circumstances in cloud computing might lead to changes in the number of goals throughout the execution of a process, providing the second source of dynamism. To tackle this issue, this research introduces a novel hybrid optimization algorithm that employs both the Firefly algorithm and the cuckoo search algorithm. The Firefly algorithm, inspired by the flashing activity of fireflies, aids in fast exploring the search space and fine-tuning solutions, whereas Cuckoo search maximizes the exploitation of the finest solutions found. This hybrid approach takes advantage of Firefly's global search capabilities and Cuckoo's local search intensification, resulting in a robust and adaptive scheduling strategy that can effectively allocate computational tasks to cloud resources, optimizing memory utilization, cost, energy consumption and makespan in dynamic cloud environments. the proposed approach FFBSO has showed remarkable results with a lowering the make span of only 10s, illustrating its extraordinary efficiency in managing tasks. Furthermore, when submitted to 400 jobs using the proposed technique, the model obtained an excellent energy consumption of 11kwh. In addition to its memory usage ability, the suggested model also succeeded in memory usage, obtaining a particularly low value of 87%. © 2023 IEEE.

关键词： Cuckoo Search algorithm Firefly algorithm hybrid optimization algorithm Makespan Workflow Scheduling

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Developing an optimal layout design of a satellite system by considering natural frequency and attitude control constraints

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AEROSPACE SCIENCE AND TECHNOLOGY 2017年 71卷 172-188页

作者： Fakoor, Mahdi Zadeh, Parviz Mohammad Eskandari, Homa Momeni Univ Tehran Fac New Sci & Technol Tehran Iran

In recent years, there has been a growing research interest in layout design optimization of satellite systems. The layout design optimization of a satellite system is a complex process having a large number of design variables and constraints. This paper presents a hybrid optimization algorithm, which globally explores the design search space using Particle Swarm optimization (PSO) and gradient-based Sequential Quadratic Programming (SQP) to rapidly locate optimum design point. The majority of the previous research works mainly focused on finding reasonable placement of components in satellite layout design, with some specific requirements, which are essential for the satellite stability, control and performance such as attitude control, non-interference and overlap constraints. In this study, additional requirements such as structural stiffness and natural frequency constraints are also considered. The proposed approach is employed on a simplified international global communication satellite. The obtained results indicate that the consideration of natural frequency and attitude control constraints in the configuration layout design of a satellite system can significantly improve the stability and control of the satellite and thus frequency coupling between satellite and launcher can be prevented. In addition, the results indicate that the proposed method provides an effective way of solving layout design optimization problem of satellite systems. (C) 2017 Elsevier Masson SAS. All rights reserved.

关键词： Global optimization Layout design optimization hybrid optimization algorithm Sequential quadratic programming (Su) Particle swarm optimization (PSO)

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hybrid Differential Evolution algorithm Employed for the Optimum Design of a High-Speed PMSM Used for EV Propulsion

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2017年第12期64卷 9824-9833页

作者： Fodorean, Daniel Idoumghar, Lhassane Brevilliers, Mathieu Minciunescu, Paul Irimia, Cristi Tech Univ Cluj Napoca Dept Elect Machines & Drives Cluj Napoca 400114 Romania Univ Haute Alsace Math & Appl Informat Lab F-68100 Mulhouse France ICPE SA Bucharest 030138 Romania Siemens Ind Software Brasov 500203 Romania

This paper presents a new optimization approach, based on a hybrid algorithm, used for the design of a new high-speed permanent magnet synchronous machine (HS-PMSM). The HS-PMSM is used for the electromagnetic propulsion system (i.e., the machine will be connected to a magnetic gear) of an electric vehicle. The hybrid optimization algorithm combines two differential evolution methods and a memory mechanism, in order to increase the quality of the algorithm. The evolution of the rotor structure of the HS-PMSM (running at 22 000 r/min) is presented, as well as some aspects of the mechanical risks due to centrifugal forces. The best suited variant will be optimized based on the new optimization approach and the solution will be numerically and experimentally validated.

关键词： Electromagnetic propulsion system for electric vehicle (EV) high-speed permanent magnet synchronous machine (HS-PMSM) hybrid optimization algorithm

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A new modeling and solution method for optimal energy flow in electricity-gas integrated energy system

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2019年第9期43卷 4322-4343页

作者： Tan, Yuhua Wang, Xin Zheng, Yihui Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

With the increasing interdependency of electricity and gas, it is necessary to simultaneously investigate electric power system and natural gas system from the perspective of an electricity-gas integrated energy system (EGIES). As an extension and integration of both optimal power flow (OPF) and optimal gas flow (OGF), optimal energy flow (OEF) is regarded as the cornerstone of the EGIES and lays an essential foundation for further research on the EGIES's operation and analysis considering stochastic conditions and contingency states. The objective of this paper is to develop a generalized mathematical model and a universally applicable simulation tool for the OEF problem. First, natural gas system is modeled in a way similar to electric power system according to electricity-gas analogy analysis, where gas admittance, gas nodal admittance matrix, and the nodal equation of gas flow conservation are derived. Then, a generalized accurate OEF model is formulated by simultaneously integrating the OPF model and the OGF model as well as their coupling constraints in a unified modeling framework. Furthermore, an available hybrid optimization approach consisting of whale optimization algorithm, MATPOWER, hydraulic calculation iterative program, and nonstationary penalty function method is put forward to solve the OEF problem. The accuracy, feasibility, and applicability of the proposed modeling and solution method is finally demonstrated by analyzing Belgian 20-node gas system combined with IEEE 30-bus test system.

关键词： electric power system hybrid optimization algorithm integrated energy system natural gas system optimal energy flow optimal power flow optimal gas flow

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A hybrid equilibrium optimizer algorithm for multi-level image segmentation

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第4期18卷 4648-4678页

作者： Qi, Hong Zhang, Guanglei Jia, Heming Xing, Zhikai Northeast Forestry Univ Sch Informat & Comp Engn Harbin Heilongjiang Peoples R China Sanming Univ Sch Informat Engn Sanming Fujian Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan Peoples R China

Threshlod image segmentation is a classic method of color image segmentation. In this paper, we propose a hybrid equilibrium optimizer algorithm for multi-level image segmentation. When multi-level threshold method calculates the neighborhood mean and median of color image, it takes huge challenge to find the optimal threshold. We use the proposed method to optimize the multi-level threshold method and get the optimal threshold from the color image. In order to test the performance of the proposed method, we select the CEC2015 dataset as the benchmark function. The result shows that the proposed method improves the optimization ability of the original algorithm. And then, the classic images and wood fiber images are taken as experimental objects to analyze the segmentation result. The experimental results show that the proposed method has a good performance in Uniformity measure, Peak Signal-to-Noise Ratio and Feature Similarity Index and CPU time.

关键词： wood fiber image segmentation 3DOtsu equilibrium optimizer algorithm grasshopper optimization algorithm hybrid optimization algorithm

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Optimized Tilt Fractional Order Cooperative Controllers for Preserving Frequency Stability in Renewable Energy-Based Power Systems

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IEEE ACCESS 2021年 9卷 8261-8277页

作者： Elmelegi, Ahmed Mohamed, Emad A. Aly, Mokhtar Ahmed, Emad M. Mohamed, Al-Attar Ali Elbaksawi, Osama Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Univ Tecn Federico Santa Maria Elect Engn Dept Valparaiso 2390123 Chile Jouf Univ Coll Engn Dept Elect Engn Sakaka 2014 Saudi Arabia Aswan Univ Fac Engn AWCRC Aswan 81542 Egypt Port Said Univ Fac Engn Dept Elect Engn Port Said 42526 Egypt

The low system inertia and the high sensitivity to load and generation fluctuations represent the main challenges for future ambitious plans of modern power systems accompanied by high penetrations levels of the renewable energy sources (RESs). Therefore, this article presents a new approach for solving the load frequency control (LFC) in addition to the virtual inertia control (VIC) in interconnected RESs penetrated power systems using cooperative tilt-based controllers and a hybrid modified particle swarm optimization with genetic algorithm (MPSOGA). The VIC system is adopted using superconducting magnetic energy storage (SMES) to provide sufficient inertial energy for system stability. Two tilt-based controllers are employed in each area using the tilt-integral-derivative (TID) controller for the SMES and TID with filter (TIDF) for the LFC function. The cooperative optimum design of the TID/TIDF controllers leads to the enhancement of frequency stability in studied two-area power systems. The formulated optimization process aims to minimize the frequency nadir settling time during abrupt changes of RESs and/or load changes, considering the cooperative control of LFC and VIC. The proposed approach has been applied to a case study consisting of two-area power systems, connected via hybrid high voltage DC/AC (hybrid HVAC/HVDC) tie-line, integrated with distributed conventional generations, photovoltaic (PV), and wind generation systems. Performance analysis has been conducted to demonstrate the effectiveness of the proposed method is compared to the genetic algorithm (GA) and particle-swarm optimization (PSO) using high fluctuations of renewable generations under extreme changes in loading conditions and physical parameters variation. The obtained results show the superiority of MPSOGA approach on the other competitive optimization techniques.

关键词： Licenses hybrid optimization algorithm interconnected power systems load frequency control renewable energy sources (RESs) superconducting magnetic energy storage (SMES) virtual inertia control

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A solution to the ORPD problem and critical analysis of the results

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ELECTRICAL ENGINEERING 2018年第1期100卷 253-265页

作者： Radosavljevic, Jordan Jevtic, Miroljub Milovanovic, Milo Univ Pritina Kosovska Mitrovica Fac Tech Sci Kneza Milosa St 7 Kosovska Mitrovica 38220 Serbia

One of the most important conditions for economic and secure operation of electric power system is the optimal reactive power dispatch (ORPD). The ORPD is achieving by appropriate coordination of the equipments which manage the reactive power flows to minimize the real power loss and/or improve the voltage profile of the power system. Mathematically, the ORPD problem can be formulated as a nonlinear optimization problem with constraints. In this paper, a hybrid PSOGSA algorithm is proposed for solving the ORPD problem. The results obtained by hybrid PSOGSA on standard IEEE 30-bus and IEEE 118-bus test systems are compared with other meta-heuristic optimization methods. In addition, a critical review of the ORPD results published in the recent literature is presented also.

关键词： Optimal reactive power dispatch hybrid optimization algorithm Power loss minimization, voltage profile improvement

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