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检索条件"主题词=Whale optimization algorithms"

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ADM1-Guided optimization of anaerobic digestion Model Parameters via whale optimization Algorithm

ADM1-Guided Optimization of anaerobic digestion Model Parame...

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Advances in Electrical and Communication Technologies (ICAECOT), International Conference on

作者： Messili Oualid Chaabna Abdelhani Semcheddine Samia Power Electronic and Industrial Control Laboratory (LEPCI) UFAS Setif1 Setif Algeria

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

Anaerobic Digestion (AD) technology, known for its waste recycling and environmental clean-up capabilities, also generates renewable energy in the form of biogas. The need to reduce dependence on fossil fuels has extensively stimulated interest in this process. The process is known for its complexity, high sensitivity, and potential for instability, which is inherently reflected in the models describing it. Models contain uncertain parameters and highly sensitive to process noise. Parameter identification plays a crucial role during modeling and is a delicate task preceding control law development. It is mandatory to have the best possible estimates of a model that guarantees efficient predictions. This paper focuses on the optimization of stoichiometric and kinetic coefficients of the Acidogenesis Methanogenesis 2 model (AM2). We present in this study the use of the whale optimization Algorithm (WOA) to optimize AM2 model parameters and validate the results using data acquired from a Anaerobic Digestion Model $\mathbf{N}^{\circ} 01$ (ADM1). The identification algorithm was implemented in MATLAB 2018. To validate the effectiveness of our proposed approach, we conduct a comparative analysis with Genetic Algorithm (GA). The results, despite certain limitations, underscore the robustness of WOA for parameter estimation.

关键词： Renewable energy sources Parameter estimation Sensitivity Biological system modeling whale optimization algorithms Data models Robustness Recycling optimization Genetic algorithms

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Multi objective non dominated sorting whale optimization genetic algorithm for convolutional neural network-based on-chip networks

Multi objective non dominated sorting whale optimization gen...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Zuda Liang Feng Hu Bangjian Xu Chunyuan Wei National Supercomputing Center in Zhengzhou Zhengzhou University Zhengzhou China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In recent years, chip technology has experienced rapid development, achieving the integration of multiple processing units and driving the development of modern multi-core systems. Network on Chip (NoC) has been widely adopted as an interconnect communication architecture superior to traditional buses. In the realm of artificial intelligence, the widespread application of convolutional neural networks has brought enormous computational demands, while traditional computers have relatively slow processing speeds. In order to address this challenge, people have begun to develop convolutional neural network accelerators, namely artificial intelligence chips, which use on-chip networks to achieve efficient data transmission and communication. In order to effectively and efficiently map convolutional neural networks onto on-chip networks and achieve goals such as low power consumption and low latency, a multi-objective mapping method for non dominated sorting whale optimization genetic algorithm is proposed by utilizing the strong global search ability of whale optimization algorithm and combining it with non dominated sorting algorithm, aiming to solve the mapping problem of on-chip networks. The experimental results demonstrate that the proposed method ∊an achieve reduced power consumption and latency.

关键词： Seminars Power demand Search problems whale optimization algorithms System-on-chip Convolutional neural networks Artificial intelligence

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SURAKSIT: A Secure Resource Allocation Scheme Using whale optimization for D2D Communication

SURAKSIT: A Secure Resource Allocation Scheme Using Whale Op...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Nilesh Kumar Jadav Sudeep Tanwar Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

As the demand for mobile data traffic continues to rise, short-range communication, such as device-to-device (D2D) communication, will become a paramount research focus. D2D communication is severely strained by distinct challenges, such as interference, resource allocation, and security, that degrade its performance in terms of sum rate, resource allocation fairness, and system throughput. In this article, we aim to maximize the overall system throughput of the D2D communication by employing a bio-inspired algorithm - whale optimization algorithm (WOA). It uses whales’ social behaviour to detect the optimal D2D users based on their signal-to-noise-plus-interference ratio (SINR) values. Furthermore, we employed an isolation forest algorithm with the intent of identifying potential eavesdroppers who illicitly augment their resources (transmission power) to intercept ongoing D2D communication. The isolation forest algorithm offers anomaly scores that identify deviations in their resources, signalling the presence of entities that may be eavesdropping. The WOA-based access control restricts the eavesdropper and allows only the optimal D2D users to participate in the resource allocation process, thus improving the throughput of the D2D communication. Lastly, the proposed scheme is assessed using different assessment metrics, such as fairness (0.861), system throughput (28.67 Mbps), and convergence rate.

关键词： Access control Performance evaluation Process control Interference Forestry Throughput whale optimization algorithms

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A Hybrid Multi-Strategy Improved Dung Beetle optimization Algorithm for Global optimization Problems

A Hybrid Multi-Strategy Improved Dung Beetle Optimization Al...

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Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Zheyi Wang Aosheng Xing Jie Zhang College of Computer and Information Science Chongqing Normal University Chongqing China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

Dung Beetle Optimizer (DBO) is a meta-heuristic algorithm formed by modeling inspired by living creatures and is known for its fast convergence. However, it still suffers from poor global exploration abilities, a proclivity to settle for local optimums and a lack of population variety. Therefore, this paper proposes a hybrid beluga whale optimization (BWO) and dung beetle optimization algorithm with somersault foraging (SRBDBO) using three strategies to improve the DBO. First, the interaction behavior between individuals in the hybrid BWO exploration phase enhances the global exploration ability of the rolling dung beetle, and the diversity of the dung beetle population is improved by mixing the BWO whale-fall phase at the end of each iteration of the population. Second, a somersault foraging strategy is introduced to expand the foraging area of small dung beetles. An adaptive somersault factor is also incorporated to balance global exploration and local exploitation and prevent the population from stopping updating at a locally optimal solution. Third, refraction opposition-based learning (ROBL) is used to reduce the likelihood that a thief dung beetle will stagnate updating in a locally optimal region. Finally, the widely recognized CEC2017 benchmark function set is utilized to assess the performance of SRBDBO. The simulation experiments show that SRBDBO demonstrates strong optimization-seeking performance and robustness when compared with the selected algorithm.

关键词： Diversity reception Metaheuristics Learning (artificial intelligence) Benchmark testing whale optimization algorithms Stability analysis Robustness Internet of Things Thermal stability Convergence

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Intelligent optimization design of antipodal Vivaldi antenna for partial discharge detection of cables

Intelligent optimization design of antipodal Vivaldi antenna...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Zhenan Luo Chengyi Liang Huawei Xue Zhongjin Tan Yao Fu Yi Feng Chao Yuan Yangjiang Power Supply Bureau Guangdong Power Grid Limited Liability Company Yangjiang China College of Electrical and Information Engineering Hunan University Changsha China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In this study, a modified non-dominated sorting whale optimization algorithm (MNSWOA) is utilized for optimizing UHF antennas for partial discharge (PD) detection to satisfy the requirements of minimal return loss and maximal gain in the target frequency band. Based on an elite retention strategy, MNSWOA, significantly improving algorithm optimization and increasing population diversity, employs the one-point binary crossover and polynomial variation. Then, a Pareto optimal solution is selected as the proposed UHF antenna. Finally the experimental results demonstrate that the antenna achieves a reflection coefficient of less than -10 dB and an average gain of 4.4 dBi within the frequency range from 1 GHz to 3 GHz.

关键词： Partial discharges Vivaldi antennas UHF antennas Pareto optimization whale optimization algorithms Polynomials Gain optimization Standards Sorting

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A New Belief Rule Base Considering Explainability optimization for Complex System Health State Assessment

A New Belief Rule Base Considering Explainability Optimizati...

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Chinese Automation Congress (CAC)

作者： Chao Chen You Cao Zhixiang Chen Ruiqiao Yao Hongbin Shu Junliang Han High-tech Institute Qingzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Health status assessment of complex systems is a critical method to ensure their safety. The belief rule base (BRB) is essentially a white-box expert system. However, experts cannot fully understand the mechanism of complex systems, the parameters of the BRB need to be adjusted through optimization algorithms. Due to the optimization algorithm has a random exploration mechanism, the optimized expert knowledge cannot be effectively retained, which reduces the explainability of BRB. Therefore, a new belief rule base considering explainability optimization (BRB-EO) for complex system health status assessment is proposed. In BRB-EO model, a modified whale optimization algorithm (WOA) for explainable BRB is proposed. Firstly, a new scattering method centered on expert knowledge is proposed to effectively utilize expert knowledge. Then, an explainability constraint is proposed to preserve the expert knowledge after optimization. Finally, the effectiveness of the BRB-EO is demonstrated through a NASA lithium battery case study.

关键词： Deep learning Representation learning Scattering Feature extraction Lithium batteries whale optimization algorithms Safety Data mining Complex systems optimization

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Economic Load Dispatch in Microgrid Using whale optimization Algorithm

Economic Load Dispatch in Microgrid Using Whale Optimization...

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India Council International Subsections Conference (INDISCON), IEEE

作者： Himanshu Raj Supriya Jaiswal Electrical Engineering Department National Institute of Technology Hamirpur Hamirpur India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

In recent years, the integration of renewable energy generation into microgrids has significantly increased. This trend highlights the critical need to optimize power generation while minimizing costs. As a response, this paper proposes the whale optimization Algorithm (WOA) as a solution for addressing the economic load dispatch (ELD) problem within microgrids. Microgrids, characterized by distributed energy resources and connected loads, operate as unified entities within defined electrical boundaries. They encompass diverse micro-sources, including distribution generators, solar units, and wind turbines, alongside various load types. Implemented using MATLAB, the WOA is applied to tackle the ELD problem within the microgrid context. The study systematically evaluates the algorithm’s performance across varying power demands over a 24-hour period. Results from the WOA implementation demonstrate significant cost reductions, surpassing the performance of other optimization algorithms such as the Cuckoo Search Algorithm (CSO), Differential Evolution (DE), and Particle Swarm optimization (PSO). This outcome underscores the efficacy of the proposed approach in optimizing economic load dispatch within microgrids.

关键词： Renewable energy sources Costs Power demand Wind energy Microgrids Solar energy whale optimization algorithms Wind turbines Stakeholders optimization

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Improving Credit Score Classification Using Long Short-Term Memory and Support Vector Machines Tuned with whale optimization Algorithm

Improving Credit Score Classification Using Long Short-Term ...

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Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Sebastianus Radhya Yeremia Marcellius Toemali Francesco Jovan Abram Setyo Prabowo Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

Credit scoring is important in the financial industry. While traditional credit scoring has been shown to be useful, machine learning is being explored as an alternative by their ability to find complex patterns that traditional models might miss. This study aims to use the whale optimization Algorithm (WOA) for hyperparameter tuning to improve the performance of the Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) model in credit score classification, precisely on the Kaggle Credit Score Classification dataset. Hyperparameter tuning is crucial but can be time-consuming and lead to poor selection. The results show WOA's success in optimizing both models with a performance increase of 1- 4% on each evaluation metric. WOA also surpassed Genetic Algorithm (GA) in tuning LSTM and was comparable to GA in tuning SVM. Despite the study's constraints, WOA found more optimal hyperparameters for each model and outperformed GA, indicating the potential for improved credit scoring.

关键词： Support vector machines Measurement Technological innovation whale optimization algorithms Vectors Classification algorithms Tuning Long short term memory optimization Genetic algorithms

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Fractional Order PID Control Based on Improved whale optimization Algorithm

Fractional Order PID Control Based on Improved Whale Optimiz...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Yanli Zhang Qi Lu Panjin Institute of Industrial Technology Dalian University of Technology Panjin China Dandong Dongfang Measurement and Control Technology Co. Ltd Dandong China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

According to the design problem of fractional order PID controller, Adaptive Mixed Strategy whale optimization Algorithm (AMSWOA) is proposed to adjust the parameters of fractional order PID controller. The tent chaotic sequence is introduced into whale optimization algorithm (WOA) to improve the population diversity global search ability and convergence speed of WOA. The nonlinear variation strategy of convergence factor is used, and adaptive weight is added to solve the problem that the algorithm falls into the local optimal solution. The addition of Gaussian mutation helps to improve the search accuracy and optimization performance of the algorithm. Optimize the 10 benchmark functions, the results show that compared with WOA algorithm, GWO and PSO optimization algorithm, AMSWOA has a great improvement in optimization performance. Finnally, the integral and fractional controlled systems are simulated and analyzed. The simulation results show that the proposed algorithm has advantages such as little overshoot, fast rising velocity and short settling time, etc.

关键词： Accuracy Chaotic communication Simulation Sociology Benchmark testing whale optimization algorithms Steady-state

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Multi-User Computation Offloading in Mobile Edge Computing with Hybrid whale optimization

Multi-User Computation Offloading in Mobile Edge Computing w...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jing Bil Ning Li Haitao Yuan Jia Zhang MengChu Zhou College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the increasing amount of data and the need for real-time processing, Mobile Edge Computing (MEC) is growing rapidly, driving the shift from traditional cloud computing to distributed edge architectures. When offloading these applications with large amounts of data on mobile devices, a lot of computing and storage resources and high energy consumption are required. Yet, mobile devices' computing power, resource storage, and battery power are often limited and cannot meet these needs. To solve a computation offloading problem for joint optimization of time, cost, and energy, this work proposes an improved hybrid algorithm called Chaos and Lévy flights-based whale optimization Algorithm (CLWOA) to solve the multi-user offloading problem in an MEC-Cloud system. Each task is offloaded to local processors of mobile devices, edge servers, and cloud servers in proportion to jointly minimize the completion time, energy consumption, and total cost. Finally, compared with the whale optimization algorithm, lévy flight whale optimization algorithm, refined whale optimization algorithm, and chaos-based whale optimization algorithm, CLWOA reduces the weighted cost by 1.89%, 0.31%, 0.19%, and 0.42%, respectively.

关键词： Chaos Energy consumption Cloud computing Costs Multi-access edge computing whale optimization algorithms Mobile handsets User experience Servers optimization

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