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Multi-objective optimization of airfoils with integral tubular high-pressure tanks for hydrogen storage

AEROSPACE SCIENCE AND TECHNOLOGY 2024年 155卷

作者： Dexl, Florian Hauffe, Andreas Markmiller, Johannes TUD Dresden Univ Technol Inst Aerosp Engn D-01062 Dresden Germany

Reducing the environmental impact of air transport is one of today's most important challenges in aviation industry and research. A promising key enabler is the use of hydrogen as an alternative to fossil fuels. The development of hydrogen-powered aircraft poses new engineering challenges due to its low volumetric energy density requiring high-pressure or cryogenic storage. If the volume in the wing shall be further used for storing hydrogen under high pressure, new demands arise to airfoil design. The present work focuses on this issue by presenting a multi-objective optimization approach aiming for airfoils with both low drag and high volume for internal tubular high-pressure tanks. This allows to directly address the new design objective and to find novel airfoil shapes providing the best compromise between aerodynamic efficiency and high storage volume for pressurized hydrogen. The resulting optimization problem is solved using evolutionary algorithms. For an efficient aerodynamic evaluation, the open source viscous-inviscid panel method XFOIL is used. An application example, based on the flight conditions of a general aviation aircraft, demonstrates the applicability of the method. Comparisons of the resulting aerodynamic characteristics obtained by XFOIL with RANS simulations confirm the feasibility of the results.

关键词： Airfoils Aerodynamic optimization Pressure tanks Hydrogen storage evolutionary algorithms Class-shape transformation

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Metaheuristic algorithms in Smart Farming: An Analytical Survey

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IETE TECHNICAL REVIEW 2024年第1期41卷 46-65页

作者： Mishra, Aishwarya Goel, Lavika Malaviya Natl Inst Technol Jaipur Dept Comp Sci & Engn Jaipur 302017 India

The techniques for solving complex optimization problems using nature inspired metaheuristic algorithms are widely accepted. Nature inspired methods use nature derived approaches to offer an efficient solution within polynomial time. This paper presents analytics of some of the significant nature inspired metaheuristic algorithms. It elaborates on the principles and concepts that are used in these algorithms representing their similarities, variations, and exceptions. The taxonomical classification and analytics presented in this paper list the nature derived phenomenon used to develop a wide variety of nature-inspired techniques. The algorithms are classified as per the type of agents used, search techniques, sub-optimization methods, type of constraints, and nature of problems. The survey comprehends the use of control parameters like exploration and convergence applicable to these algorithms and their domain specifications. The sources of nature inspiration are also presented with their variants. It establishes the analytics required to choose a specific nature-inspired heuristic algorithm for smart farming and related applications. Metaheuristic algorithms like Particle Swarm optimization, Ant colony optimization, Whale optimization, Firefly optimization, etc. have contributed significantly in assisting smart farming methods for better productivity of crops.

关键词： Nature inspired algorithms Optimization algorithms evolutionary algorithms Smart farming

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NeuroHAR: A Neuroevolutionary Method for Human Activity Recognition (HAR) for Health Monitoring

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IEEE ACCESS 2024年 12卷 112232-112248页

作者： Alam, Furqan Plawiak, Pawel Almaghthawi, Ahmed Qazani, Mohammad Reza Chalak Mohanty, Sanat Alizadehsani, Roohallah Sohar Univ Fac Comp & Informat Technol FoCIT Sohar 311 Oman Cracow Univ Technol Fac Comp Sci & Telecommun Dept Comp Sci PL-31155 Krakow Poland Polish Acad Sci Inst Theoret & Appl Informat PL-44100 Gliwice Poland King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Abha 62529 Saudi Arabia Liverpool John Moores Univ Sch Comp Sci & Math Liverpool L3 3AF England Deakin Univ Inst Intelligent Syst Res & Innovat IISRI Geelong Vic 3220 Australia

Human Activity Recognition (HAR) is becoming increasingly important in the fast-evolving landscapes of wearable sensors, smart applications, and the Internet of Things (IoT) paradigms. HAR is rapidly gaining importance, especially in health monitoring, elderly and infant care, fitness tracking, and security. Machine learning (ML) and Deep Learning (DL) methods are used significantly for HAR problems. ML and DL methods often face four significant problems. Firstly, they lack the adaptability to evolve network architectures dynamically, which is vital in complex tasks like HAR. Secondly, classical ML and DL methods could fall short in comprehensively navigating potential solution spaces. Thirdly, finding optimal hyperparameters is a computationally expensive process, and lastly, expert knowledge is required to configure the hyperparameters. This paper proposes the NeuroHAR, a transformative neuroevolutionary method for HAR, to address these problems. NeuroHAR integrates feedforward deep neural networks (FDNN) with evolutionary algorithms. The highlights of NeuroHAR include its dynamic optimization of network architectures and hyperparameters. It is simple to configure and computationally efficient. Due to its adaptable design, it offers a more flexible and robust solution that handles task complexities better than traditional methods. Results are promising, which is evidence of the effectiveness of the proposed NeuroHAR, which outperformed the explicit contender, the state-of-the-art Grid Search approach. NeuroHAR and Grid Search evaluated 900 and 1080 models in Case I. Even with broader hyperparameter ranges in Case II, NeuroHAR still executed 900 models, whereas Grid Search would need over 2 billion models, which proves the computational efficiency of NeuroHAR. Additionally, for HARTH and HAR70Plus imbalanced datasets, the NeuroHAR model achieved a higher prediction accuracy of 89.91% versus Grid Search's 84.04%. NeuroHAR can facilitate advanced monitoring and analy

关键词： Human activity recognition Tuning Artificial neural networks Accuracy Support vector machines Security Optimization Biomedical monitoring Deep learning evolutionary computation Internet of Things Smart cities Wearable sensors Machine learning Human activity recognition (HAR) health monitoring wearable sensors deep learning evolutionary algorithms Internet of Things (IoT) smart cities

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Weld distortion prediction of the Cargo Transfer Vessel key structure by fuzzy finite element model and inherent strain theory

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OCEAN ENGINEERING 2024年 313卷

作者： Zhou, Zhijie Cai, Shupeng Qiu, Ming Zhao, Yongping Zhang, Yongkang Lin, Chaohui Guangdong Univ Technol Sch Electromech Engn Guangzhou 510006 Peoples R China Guangdong Prov Key Lab Adv Mfg Technol Marine Ener Guangzhou 510006 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China COSCO Shipping Qidong Offshore Co Ltd Qidong 226259 Peoples R China COSCO Shipping Heavy Ind Guangdong Co Ltd Dongguan 523146 Peoples R China

This study introduces an innovative fuzzy finite element model (fFEM) based on inherent strain theory, optimized with artificial intelligence, to predict welding deformations in critical components of Cargo Transfer Vessel (CTV). By integrating a genetic algorithm with the commercial finite element software ABAQUS, optimal inherent strain coefficients are identified. The fFEM significantly reduces computation time, achieving approximately 60% efficiency compared to traditional thermo-elastic-plastic models. The model demonstrates high accuracy, with an average prediction error of less than 10%. The efficacy of this method is validated through comparative analyses against traditional models and experimental results, highlighting its precision and effectiveness in predicting welding deformations in large marine structures. This approach provides a robust framework for addressing welding deformations, ensuring structural integrity and precision in marine engineering applications.

关键词： Welding distortion Finite element analysis Inherent strain Fuzzy finite element model evolutionary algorithms

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Evolving random weight neural networks based on oversampled-segmented examples for IoT intrusion detection

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JOURNAL OF SUPERCOMPUTING 2024年第11期80卷 16393-16427页

作者： Qaddoura, Raneem Faris, Hossam Al Hussein Tech Univ Sch Comp & Informat Amman 11831 Jordan Univ Jordan King Abdullah II Sch Informat Technol Amman 11942 Jordan Univ Granada Res Ctr Informat & Commun Technol Granada Spain

The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many applications, including the smart home Internet of Things networks, where security risks threaten the privacy of individuals. In this context, many works were proposed for detecting and classifying the different types of attacks. However, many challenges are identified for this type of problem, such as the large amount of data available, the imbalanced nature of the data, and the quality of detection and classification outcomes. This paper aims to address these challenges by proposing an approach that considers a metaheuristic-based random weight neural network to detect intrusion activities and classify the different types and subtypes of activities. The following points summarize the contribution of this paper. First, the automatic tuning of the neural network parameters where the weights, biases, regularization value, the number of neurons, and the type of activation function are optimized by different metaheuristic algorithms to produce high-quality results. Second, the proposed approach adopts a clustering with reduction technique to tackle the challenge of processing large volumes of data. Third, oversampling the dataset is also embedded in the proposed approach to avoid a biased classification of the majority class. The experiments are conducted based on a large dataset with more than half a million instances. The results show that the proposed approach outperforms the other classification approaches in geometric mean (G-Mean) and has promising results.

关键词： Random weight network Synthetic Minority Oversampling TEchnique evolutionary algorithms Classification Clustering Internet of Things

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GenerativeGI: creating generative art with genetic improvement

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AUTOMATED SOFTWARE ENGINEERING 2024年第1期31卷 23-23页

作者： Fredericks, Erik M. Moore, Jared M. Diller, Abigail C. Grand Valley State Univ Sch Comp 1 Campus Dr Allendale MI 49401 USA

Generative art is a domain in which artistic output is created via a procedure or heuristic that may result in digital and/or physical results. A generative artist will typically act as a domain expert by specifying the algorithms that will form the basis of the piece as well as defining and refining parameters that can impact the results, however such efforts can require a significant amount of time to generate the final output. This article presents and extends GenerativeGI, an evolutionary computation-based technique for creating generative art by automatically searching through combinations of artistic techniques and their accompanying parameters to produce outputs desirable by the designer. Generative art techniques and their respective parameters are encoded within a grammar that is then the target for genetic improvement. This grammar-based approach, combined with a many-objective evolutionary algorithm, enables the designer to efficiently search through a massive number of possible outputs that reflect their aesthetic preferences. We included a total of 15 generative art techniques and performed three separate empirical evaluations, each of which targets different aesthetic preferences and varying aspects of the search heuristic. Experimental results suggest that GenerativeGI can produce outputs that are significantly more novel than those generated by random or single objective search. Furthermore, GenerativeGI produces individuals with a larger number of relevant techniques used to generate their overall composition.

关键词： Generative art evolutionary algorithms Grammatical evolution Genetic improvement

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An evolutionary Algorithm-Based Vehicular Clustering Technique for VANETs

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IEEE ACCESS 2022年 10卷 14368-14385页

作者： Shah, Yaser Ali Aadil, Farhan Khalil, Amaad Assam, Muhammad Abunadi, Ibrahim Alluhaidan, Ala Saleh Al-Wesabi, Fahd N. COMSATS Univ Islamabad Dept Comp Sci Attock 43600 Pakistan Univ Engn & Technol Dept Comp Syst Engn Peshawar 25000 Pakistan Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China Prince Sultan Univ Dept Informat Syst Riyadh 11586 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Arts Dept Comp Sci Abha 62529 Saudi Arabia

Many precious lives are lost world-wide due to road accidents. To counter this issue, the ultimate solution is Vehicular ad hoc networks (VANETs). Due to the high mobility of vehicles and varying network topology in VANETs, efficient communication among the vehicular nodes is of extreme importance. To enhance communication proficiency in VANETs, clustering is a renowned procedure. Therefore, a clustering algorithm based on Moth-Flame Optimization (MFO), titled AMONET, is projected which can effectively work in high mobility nodes scenario of VANETs. AMONET is based on bio inspired procedure and creates optimized clusters for reliable and efficient communication. Our algorithm is assessed experimentally with well-known procedures such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO) and Multi Objective Particle Swarm Optimization (MOPSO). Several experiments are conducted to measure the comparative efficiency of these procedures. The average cumulative results for all the grid sizes are 27.1% for AMONET while 36.3% for ACO, 54.9% for CLPSO and 58.7% for MOPSO. The results signify that AMONET produces near ideal results, covers the entire network and generates least number of clusters. It is an efficient technique to accomplish vehicular clustering with the purpose of improving the network's overall performance and consequently reducing the routing cost of the vehicular network.

关键词： Optimization Clustering algorithms Routing Particle swarm optimization Costs Roads Reliability Clustering evolutionary algorithms intelligent transportation system (ITS) moth-flame optimization (MFO) swarm-based algorithm vehicular ad hoc networks (VANETs)

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A feasibility restoration particle swarm optimizer with chaotic maps for two-stage fixed-charge transportation problems

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SWARM AND evolutionary COMPUTATION 2024年 91卷

作者： Shivani Chauhan, Dikshit Rani, Deepika Dr BR Ambedkar Natl Inst Technol Dept Math & Comp Jalandhar 144008 India

This paper delves into solving the two-stage non-linear fixed-charge transportation problem (two-stage NFCTP), where each arc is associated with fixed and variable costs that increase proportionally to the square of the units transported. The presence of fixed charges and non-linear components categorizes this problem as NP - hard, leading to computational challenges, inefficiencies, and the risk of local optima. To address these challenges, a feasibility restoration particle swarm optimizer with chaotic maps (CEPSO) is presented. The proposed algorithm introduces (i) non-linear adaptive inertia weight and acceleration coefficients to maintain better exploration and exploitation rates during the search. (ii) Ten chaotic maps are integrated into the acceleration coefficients to enhance optimization capabilities further. (iii) Feasibility restoration mechanisms, including constraint compliance adjustment and ratio adjustment procedures, are incorporated to ensure the feasibility of solutions generated by CEPSO. The algorithm's performance is evaluated across small and largescale NFCTPs, ranging from 35 to 1044 dimensions, and compared to existing PSO variants using various evaluation metrics. Experimental analyses demonstrate CEPSO's superior optimization performance for two- stage NFCTPs, positioning it as an advanced framework in this domain and contributing to the novelty of this study. The related codes can be found using this link: https://***/ChauhanDikshit.

关键词： Combinatorial optimization evolutionary algorithms Transportation Optimization

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Constrained multi-objective optimization problems: Methodologies, algorithms and applications

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KNOWLEDGE-BASED SYSTEMS 2024年 299卷

作者： Hao, Yuanyuan Zhao, Chunliang Zhang, Yiqin Cao, Yuanze Li, Zhong Fern univ Hagen Fac Math & Comp Sci D-58097 Hagen Germany Qingdao Univ Sci & Technol Sch Data Sci 99 Songling Rd Qingdao 266061 Peoples R China Minnan Normal Univ Sch Math & Stat Zhangzhou Peoples R China Beijing Jiaotong Univ Sch Syst Sci 3 Shangyuan Village Beijing 100028 Peoples R China

Constrained multi -objective optimization problems (CMOPs) are widespread in practical applications such as engineering design, resource allocation, and scheduling optimization. It is high challenging for CMOPs to balance the convergence and diversity due to conflicting objectives and complex constraints. Researchers have developed a variety of constrained multi -objective optimization algorithms (CMOAs) to find a set of optimal solutions, including evolutionary algorithms and machine learning-based methods. These algorithms exhibit distinct advantages in solving different categories of CMOPs. Recently, constrained multi -objective evolutionary algorithms (CMOEAs) have emerged as a popular approach, with several literature reviews available. However, there is a lack of comprehensive-view survey on the methods of CMOAs, limiting researchers to track the cutting-edge investigations in this research direction. Therefore, this paper reviews the latest algorithms for handling CMOPs. A new classification method is proposed to divide literature, containing classical mathematical methods, evolutionary algorithms and machine learning methods. Subsequently, it reviews the modeling and algorithms of CMOPs in the context of practical applications. Lastly, the paper gives potential research directions with respect to CMOPs. This paper is able to provide guidance and inspiration for scholars studying CMOPs.

关键词： Constrained multi-objective optimization problems evolutionary algorithms Machine learning Applications

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Performance Analysis of the (1+1) EA on Mobile Robot Path Planning

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IEEE ACCESS 2024年 12卷 159920-159934页

作者： Lai, Xinsheng Feng, Yulin Shaoxing Univ Dept Comp Sci & Engn Shaoxing 312000 Zhejiang Peoples R China

Path planning is a fundamental function of mobile robots that have been nowadays used in a variety of areas. evolutionary algorithms (EAs) have been experimentally showed to be efficient for the problem of mobile robot path planning, an NP-problem. However, there is no theoretical work about EAs on the problem. In this paper, to understand more about why EAs are efficient and to give some advice on how to design more efficient EAs for this problem, we perform the first theoretical analysis of the expected runtime of a simple EA called (1+1) EA with three different mutations for the mobile robot path planning problem in two constructed environments. In addition, anew method of producing a path from an uncomplete path is proposed, in which anew Insert process is proposed. Meanwhile, an obvious disadvantage of one of these three mutations is discovered during the expected runtime analysis in the first environment. Based on this discovery, an improved version of this mutation is proposed. In the second constructed environment, the improved mutation is proved to be superior to the others. In detail, the (1+1) EA with the improved mutation can find the shortest path in runtime O(n(5)), while the (1+1) EA with any one of the other mutations may be trapped in local optima. Experiments have been performed on the two constructed environments and six randomly generated environments with different sizes and varying obstacle densities.

关键词： Mobile robots Path planning Heuristic algorithms Runtime Planning Performance analysis Upper bound Stars Safety Polynomials Mobile robot path planning evolutionary algorithms performance analysis NP-hard problem

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