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Frigatebird optimizer: a novel metaheuristic algorithm

PHYSICA SCRIPTA 2024年第12期99卷 125233-125233页

作者： Wang, Xiaowei Huangshan Univ Sch Tourism Huangshan Anhui Peoples R China

This paper proposes a new swarm intelligence optimization algorithm- the Frigate Bird Optimizer ( FBO ) . The algorithm is inspired by the unique fl ight and foraging behaviors of frigatebirds. Its optimization process is divided into two stages: The fi rst stage simulates frigatebirds harassing other seabirds to snatch food, and the search direction and radius of individuals have randomness and uncertainty, which is conducive to global exploration;The second stage simulates frigatebirds observing large fi sh driving small fi sh to leap out of the water and preying on them, and individuals tend to gather in the optimal search direction. By simulating the behaviors of frigatebirds under different survival strategies, the algorithm achieves extensive global search in the fi rst stage and fi ne- tuned local optimization by learning information in the second stage. To evaluate its performance, 46 functions in the CEC2014 and CEC2017 benchmark test sets are selected as objective functions and compared with 9 state-of-the-art meta-heuristic algorithms. The results show that the FBO algorithm has higher performance, excellent iterative optimization ability and strong robustness, and can be applied to different optimization domains.

关键词： swarm intelligence frigatebird metaheuristic optimization exploration and exploitation optimization algorithm engineering

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Low Frequency Hierarchical Cooperative Impulse Control for Gravitational Wave Detector Formation Keeping

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第6期47卷 1167-1181页

作者： Xie, Xuan Jiang, Fanghua Li, Junfeng Tsinghua Univ Sch Aerosp Engn Beijing 100084 Peoples R China

Configuration stability is crucial for the detection accuracy of space-based gravitational wave (GW) detectors. This paper proposes a low-frequency hierarchical cooperative impulse control strategy and a high-fidelity orbit optimization method to achieve a formation of GW detectors with high detection performance. The control strategy employs a hierarchical structure to divide the configuration control into position and shape layers. In the upper layer, the position layer, a state transition tensor method is employed to analyze the sensitivity and feasible range of the formation center position on the configuration stability. In the lower layer, the shape layer, a novel consistency protocol cooperatively uses the states of all individuals in the formation to determine the control expectations. The constraint on formation position is relaxed to reduce the number of impulses, achieving a low-frequency impulse control. The optimization method, characterized by a careful selection of the optimization variables and a design of adaptive model continuation from a low-fidelity model to a high-fidelity model, is proposed to reduce the problem-solving difficulty. The proposed strategy and method are applied to the Laser Interferometer Space Antenna (LISA) mission, and the results demonstrate that the configuration stability is effectively enhanced with several low-frequency impulses.

关键词： Deci Hertz Interferometer Gravitational Wave Observatory optimization algorithm Planets Dynamic Modelling Energy Economics Control optimization Monte Carlo Simulation Spacecraft Formation Near Earth Orbit Particle Swarm optimization

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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination algorithm (KB-FPA)

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Computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination algorithm(FPA),Spark Lion Whale optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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Modeling Laminar Separation Bubbles by Field Inversion Method

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AIAA JOURNAL 2024年第1期62卷 175-192页

作者： Longobardo, Giosue Catalano, Pietro Quagliarella, Domenico Tognaccini, Renato Univ Napoles Federico II Ind Engn Dept Piazzale Tecchio 80 Naples Italy Italian Aerosp Res Ctr Fluid Mech Unit Capua Italy

This paper presents the application of a data-driven method for turbulence modeling. The main aim is to enhance the prediction capabilities of an existing Reynolds-averaged Navier-Stokes (RANS) model for laminar separation bubbles. The recently developed field inversion method has been chosen to infer corrections to the shear stress transport kappa-omega-gamma turbulence model. An innovative approach has been proposed to reduce the computational cost of the modeling procedure. The objective function makes use of the friction coefficient to compare data coming from the RANS model with reference large-eddy simulation data available in the literature. The performed test cases responded well to the optimization procedure which provided a correction field that boosts the turbulent kinetic energy within the bubble. The proposed procedure is the necessary building block to define a new turbulence model able to overcome the present limitations in simulating laminar separation bubbles.

关键词： Laminar Separation Bubble Computational Fluid Dynamics Reynolds Averaged Navier Stokes Spalart Allmaras Turbulence Model optimization algorithm Eppler Airfoil Large Eddy Simulation

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Effect of optimization Techniques on Feedback Alignment Learning of Neural Networks 5

Effect of Optimization Techniques on Feedback Alignment Lear...

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5th International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Lee, Soha Park, Hyeyoung Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea

ISBN: (纸本)9781665456456

The error backpropagation algorithm is a representative learning method that has been used in most deep network models. However, the error backpropagation algorithm, despite its decent performance, clearly has limits to its biological plausibility. Unlike the learning mechanism of the actual brain, the error backpropagation algorithm must reuse the weights used in the forward calculation for the backward error propagation. In order to overcome these limitations, the feedback alignment method, which uses a fixed random weight for the backpropagation computation, was proposed. The feedback alignment algorithm showed performances comparable to the original error backpropagation on several benchmark data sets. However, it is still in the preliminary stage of analysis, and various analysis on its learning behavior and practical efficiency are needed. In this paper, we combine feedback alignment learning method with popular optimization techniques such as RMSprop and Adam, and investigate its effect on the learning performances through computational experiments on benchmark data sets.

关键词： Error backpropagation Biological plausibility Feedback alignment Weight transport problem Random backward weight optimization algorithm

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Orbital Inspection Game Formulation and Epsilon-Nash Equilibrium Solution

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JOURNAL OF SPACECRAFT AND ROCKETS 2024年第1期61卷 157-172页

作者： Li, Zhen-Yu Zhu, Hai Luo, Ya-Zhong Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Natl Univ Def Technol Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China Chinese Acad Mil Sci Def Innovat Inst Beijing 100071 Peoples R China

This paper studies an orbital inspection game, which involves two spacecraft competing for imaging conditions in an on-orbit inspection mission. First, the main factors affecting the imaging conditions, including the sun angle, sunangle changing rate, relative distance, and distance changing rate, are analyzed to formulate a realistic multiplefactor inspection game. An approximate switching-type payoff function is specially designed to incorporate all the boundary constraints of those factors into the game model. Then, the analytical necessary conditions for the Nash equilibrium are derived and converted as a two-point boundary value problem (TPBVP). But different from conventional routes to solve the challenging TPBVP, a lighter costate optimization method is proposed, which transforms the TPBVP to a direct optimization problem by employing the conclusion that the optimal thrust directions of both sides are the same and utilizing the theory of the epsilon-Nash equilibrium. The existence of the epsilon-Nash equilibrium is proven, and the necessary conditions for a small epsilon are derived to support the method. Finally, simulations of the GEO inspection missions demonstrated the superiority of the proposed game formulation and the high efficiency and accuracy of the proposed method.

关键词： Nash Equilibrium optimization algorithm Boundary Element Method Earth Centered Inertial Transversality Condition Particle Swarm optimization Collocation Method Terminal Velocity Propellant Computer Programming and Language

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Mission-oriented dynamic reconfiguration of airborne photovoltaic array based on multidisciplinary model

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RENEWABLE ENERGY 2024年 234卷

作者： Liu, Yang Sun, Kangwen Lv, Mingyun Beihang Univ Sch Aeronaut Sci & Engn Beijing 100191 Peoples R China

The solar-powered stratospheric airship is demonstrated overwhelming superiority because of affordable cost, recyclable material and low carbon emission. Progress has been made in the schemes of energy system consisting of curved photovoltaic array, storage, and energy management devices. However, research on various flight conditions and the resulting presence of non-uniform irradiance on the airborne solar array is rare. Moreover, the methods to reduce the power losses of on-board solar array are not investigated. This paper proposed a dynamic reconfiguration method to improve the output performance of on-board solar array in different mission conditions. The partial shading conditions of airborne and ground-based solar arrays are compared. The multiple traveling salesman problem with solar array is introduced. The Simulated annealing algorithm is adopted. The real-time optimization of solar array is obtained with the dynamic reconfiguration method according to the operating condition consisting of various parameters. The results show the proposed method effectively improves the output power of solar array compared with common total-cross-tied connections. The improvements of daily output energy of the airborne array are more than 13.7 %. The work provides a conducive reference for realizing the utilization prospect of solar energy system on aerial vehicles in a low carbon future.

关键词： Solar powered vehicle Photovoltaic array reconfiguration optimization algorithm Multidisciplinary model Partial shading conditions Power improvement

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Population diversity and inheritance in genetic programming for symbolic regression

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NATURAL COMPUTING 2024年第3期23卷 531-566页

作者： Burlacu, Bogdan Yang, Kaifeng Affenzeller, Michael Univ Appl Sci Upper Austria HEAL Softwarepk 11 A-4232 Hagenberg Austria

In this work we aim to empirically characterize two important dynamical aspects of GP search: the evolution of diversity and the propagation of inheritance patterns. Diversity is calculated at the genotypic and phenotypic levels using efficient similarity metrics. Inheritance information is obtained via a full genealogical record of evolution as a directed acyclic graph and a set of methods for extracting relevant patterns. Advances in processing power enable our approach to handle previously infeasible graph sizes of millions of arcs and vertices. To enable a more comprehensive analysis we employ three closely-related but different evolutionary models: canonical GP, offspring selection and age-layered population structure. Our analysis reveals that a relatively small number of ancestors are responsible for producing the majority of descendants in later generations, leading to diversity loss. We show empirically across a selection of five benchmark problems that each configuration is characterized by different rates of diversity loss and different inheritance patterns, in support of the idea that each new problem may require a unique approach to solve optimally.

关键词： Genetic programming Symbolic regression Supervised learning Explainable AI optimization algorithm Evolutionary algorithm

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DDoS attack detection in cloud using ensemble model tuned with optimal hyperparameter

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2024年第5期38卷 1594-1620页

作者： Reddy, K. Balachandra Meera, S. Vels Inst Sci Technol & Adv Studies VISTAS Dept Comp Sci & Engn Chennai Tamil Nadu India Vels Inst Sci Technol & Adv Studies VISTAS Dept Comp Sci & Engn Chennai 600117 Tamil Nadu India

DDoS attacks are a type of cloud incursion that lessen service degradation. DDoS attacks target the cloud network with invalid requests, rejecting legitimate requests. Such attacks disrupt the entire cloud architecture, thus it needs efficient detection methods to spot their presence. This study proposes a novel ensemble classification model for DDoS incursion detection. Pre-processing, feature extraction, and attack detection are the three main components of the suggested intrusion detection system. Improved data imbalance processing is processed during pre-processing. Features including HOS-based features enhanced entropy-based features, correlation features, and raw features are extracted from the pre-processed data. The generated features are trained using an ensemble model, which integrates classifiers like SVM, RF, NN, LSTM, and DRN, during the attack detection phase. A new hybrid approach known as TUDMA optimally trains the model by setting the ideal weight because DRN provides the final detected outcome. By reducing errors, this ideal training will guarantee an improvement in detection results. The suggested hybrid optimization combines the two techniques, TDO and DMO. The accuracy of the methods FS-WOA-DNN, RBF-PSO, SMA, BRO, SLO, and NMRA was 86.73%, 85.69%, 84.58%, 87.43%, 88.91%, and 89.78%, respectively, while the accuracy of the TUDMA was 95.34% in the 80th learning percentage. Finally, for many measurements, the suggested efficiency is superior to the traditional methods.

关键词： cloud DDoS ensemble model optimization algorithm statistical features

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Moving object detection in gigapixel-level videos using manifold sparse representation

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第6期83卷 18381-18405页

作者： Liu, Jingjing Feng, Manlong Gu, Dongzhou Zeng, Xiaoyang Liu, Wanquan Xiu, Xianchao Shanghai Univ Sch Microelect Shanghai Key Lab Chips & Syst Intelligent Connecte Shanghai Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Fudan Univ Sch Microelect State Key Lab Integrated Chips & Syst Shanghai Peoples R China Sun Yat sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China

Moving object detection (MOD) is one of the most important and challenging tasks in analyzing videos. Recently, emerging gigapixel-level videos have attracted considerable attention due to its large field of view and high spatial resolution. So far, there is not much research on moving object detection in the field of gigapixel-level videos. To detect moving objects in gigapixel-level videos, we propose a novel manifold sparse representation (MSR) method for detecting moving objects in gigapixel-level videos. The innovation of this method lies in introducing sparse representation to promote global structure and utilizing manifold learning to retain the local structure of moving objects. Then, to solve the explicit solutions of this problem, an efficient optimization scheme based on the manifold alternating direction method of multipliers (MADMM) is developed. Finally, the extensive experiments on three challenging benchmark MOD datasets, three samples from the gigapixel-level PANDA dataset (gigaPixel-level humAN-centric viDeo dAtaset), and one real wide area video dataset are executed to demonstrate the superiority of the proposed MSR. In particular, the detection performance on PANDA is improved by an average of 5%, which suggests that the proposed method is promising for gigapixel-level videos.

关键词： Gigapixel-level videos Manifold sparse representation (MSR) Moving object detection optimization algorithm

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