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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

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computer Modeling in Engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

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computers, Materials & Continua 2024年第4期79卷 1281-1299页

作者： JunWang Linxi Zhang Hao Zhang Funan Peng Mohammed A.El-Meligy Mohamed Sharaf Qiang Fu College of Computer Science and Technology Shenyang University of Chemical TechnologyShenyang110142China Key Laboratory of Intelligent Technology for Chemical Process Industry of Liaoning Province Shenyang University of Chemical TechnologyShenyang110142China Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and *** multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective *** seen,a variety of decision variable groupingalgorithms have been ***,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto *** solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is *** algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision *** the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the *** validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

关键词： Multi-objective evolutionary optimization algorithm decision variables grouping extreme point pareto frontier

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Interaction model estimation-based robotic force-position coordinated optimization for rigid-soft heterogeneous contact tasks

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Biomimetic Intelligence & Robotics 2025年第1期5卷 77-86页

作者： Haochen Zheng Xueqian Zhai Hongmin Wu Jia Pan Zhihao Xu Xuefeng Zhou Institute of Intelligent Manufacturing Guangdong Academy of SciencesGuangdong Key Laboratory of Modern Control TechnologyGuangzhou 510070China School of Rail Transportation Wuyi UniversityJiangmen 529020China Department of Computer Science The University of Hong KongHong Kong 999077China

Inspired by Model Predictive Interaction Control(MPIC),this paper proposes differential models for estimating contact geometric parameters and normal-friction forces and formulates an optimal control problem with multiple constraints to allow robots to perform rigid-soft heterogeneous contact *** the MPIC,robot dynamics are linearized,and Extended Kalman Filters are used for the online estimation of geometry-aware ***,a geometry-aware Hertz contact model is introduced for the online estimation of contact *** then implement the force-position coordinate optimization by incorporating the contact parameters and interaction force constraints into a gradient-based optimization *** validations were designed for two contact modes:“single-point contact”and“continuous contact”,involving materials with four different Young’s moduli and tested in human arm“relaxation-contraction”*** indicate that our framework ensures consistent geometry-aware parameter estimation and maintains reliable force interaction to guarantee *** method reduces the maximum impact force by 50%and decreases the average force error by 42%.The proposed framework has potential applications in medical and industrial tasks involving the manipulation of rigid,soft,and deformable objects.

关键词： Heterogeneous contact Interaction model estimation Coordination optimization Model Predictive Control

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AN ADAPTIVE POLICY TO EMPLOY SHARPNESS-AWARE MINIMIZATION 11

AN ADAPTIVE POLICY TO EMPLOY SHARPNESS-AWARE MINIMIZATION

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11th International Conference on Learning Representations, ICLR 2023

作者： Jiang, Weisen Yang, Hansi Zhang, Yu Kwok, James Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Peng Cheng Laboratory China

Sharpness-aware minimization (SAM), which searches for flat minima by min-max optimization, has been shown to be useful in improving model generalization. However, since each SAM update requires computing two gradients, its computational cost and training time are both doubled compared to standard empirical risk minimization (ERM). Recent state-of-the-arts reduce the fraction of SAM updates and thus accelerate SAM by switching between SAM and ERM updates randomly or periodically. In this paper, we design an adaptive policy to employ SAM based on the loss landscape geometry. Two efficient algorithms, AE-SAM and AE-LookSAM, are proposed. We theoretically show that AE-SAM has the same convergence rate as SAM. Experimental results on various datasets and architectures demonstrate the efficiency and effectiveness of the adaptive policy. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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FESR - Framework to evaluate Software Reliability based on software metrics 24

FESR - Framework to evaluate Software Reliability based on s...

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8th International Conference on High Performance Compilation, Computing and Communications, HP3C 2024

作者： Fahad, Hurriya Ahmed, Irfan Mahmood, Jabar Saman Batool, Khadija Ali, Qasim Department of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310007 China

ISBN: (纸本)9798400716904

Evaluating reliability of software is hot concern for decision makers and software engineers seeing as if we assess, it cannot be mastered. It is common that reliability of system stratum could be employed for evaluating growth of testing of system with help of comparison of failure of currently implemented software with required system. It is also generic that reliability of system echelon assures improved utilization by the user consequently enhanced contentment of customers. In this paper, our goal is to put forward a meticulously analysis of reliability measurement process. As the existing approaches still lack software reliability, quality and its growth, successful generation of test cases, scalability with large software systems, cost and customer satisfaction. we have projected a new framework of software reliability measurement process based on software metrics. Our model is evaluated by comparing it with an existing model and a published case study. The control experiment is used to evaluate FESR Framework. © 2024 ACM.

关键词： Software quality

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Zero-phase CARIMA Filtering and Application in Wind-storage system Sizing and Power Dispatch Optimization

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CSEE Journal of Power and Energy systems 2024年第6期10卷 2283-2295页

作者： Wei Wang Peng Chen Guorui Ren Jizhen Liu Fang Fang Zhe Chen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology Aalborg University9220 AalborgDenmark

Hybrid energy storage system(HESS)is an effective way to mitigate wind power fluctuations on multi-time scale,and can improve influence of large-scale grid-connected wind power on stability and reliability of power system.A novel methodology named zero-phase controlled auto-regressive integrated moving-average(CARIMA)filter is proposed to integrate HESS to smooth wind power ***,a design method for zero-phase CARIMA filter is provided,and then used to determine grid-connected power for a wind storage system and size *** reasons,direct current(DC)component caused by energy storage efficiency and grid-connected power delay caused by phase shift,for causing superfluous energy storage configuration are *** addition,a nonlinear programming scheduling strategy considering battery degradation is *** imbalance caused by efficiency difference during dynamic adjustment of energy storage output power is ***,thermostatically controlled loads(TCLs)are integrated in sizing and scheduling HESS to reduce energy storage demand and improve operating conditions of energy ***,effectiveness of the proposed strategy is verified by a case study.

关键词： CARIMA demand response energy storage optimization wind power zero-phase

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Vectorial liquid‑crystal holography

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eLight 2024年第1期4卷 251-261页

作者： Ze‑Yu Wang Zhou Zhou Han Zhang Yang Wei Hong‑Guan Yu Wei Hu Wei Chen Hai‑Tao Dai Ling‑Ling Ma Cheng‑Wei Qiu Yan‑Qing Lu National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and ManipulationCollaborative Innovation Center of Advanced MicrostructuresCollege of Engineering and Applied SciencesNanjing UniversityNanjing 210023China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Tianjin Key Laboratory of Low Dimensional Materials Physics and Preparing Technology School of ScienceTianjin UniversityTianjin 300072China

Vectorial optics with fine inhomogeneous polarization control are highly *** have been captivated a promising candidate,but their static post-fabrication geometry largely limits the dynamic *** crystal(LC)is usually employed as an additional index-changing layer together with ***,most of the reported LCs only impart a varying but uniform phase on top of that from the metasurface,which we term“scalar”LC ***,we pixelate a single-layer LC to display versatile and tunable vectorial holography,in which the polarization and amplitude could be arbitrarily and independently controlled at varying spatial ***,the subtle and vectorial LC-holography highlights the broadband and electrically-switchable *** vectorial LC holography reveals significant opportunities for advanced cryptography,super-resolution imaging,and many other applications.

关键词： Liquid crystals Vectorial optics Holography

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A Composite Decomposition Method for Large-Scale Global Optimization

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6304-6319页

作者： Tian, Maojiang Chen, Minyang Du, Wei Tang, Yang Jin, Yaochu Yen, Gary G. Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China East China University of Science and Technology Engineering Research Center of Process System Engineering Ministry of Education Shanghai200237 China Westlake University School of Engineering Hangzhou310030 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74048 United States

Cooperative coevolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of nonadditively separable functions, it suffers from high computational complexity. To address this challenge, this article proposes a composite separability grouping (CSG) method, seamlessly integrating DG and GSG into a problem decomposition framework to utilize the strengths of both approaches. CSG introduces a step-by-step decomposition framework that accurately decomposes various problem types using fewer computational resources. By sequentially identifying additively, multiplicatively and generally separable variables, CSG progressively groups nonseparable variables by recursively considering the interactions between each nonseparable variable and the formed nonseparable groups. Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a nonseparable variable grouping method. These two methods are designed to effectively detect multiplicatively separable variables and efficiently group nonseparable variables, respectively. Extensive experimental results demonstrate that CSG achieves more accurate variable grouping with lower computational complexity compared to GSG and state-of-the-art DG-series designs. © 2024 IEEE.

关键词： Computational efficiency

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Research on Multi-modal Multi-objective Path Planning by Improved Ant Colony Algorithm 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Jing, Juan Zhang, Ling Shen, Chaonan Zhang, Kai School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430065 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

A research about the path planning problem has been popular topic nowadays and some effective algorithms have been developed to solve this kind of problem. However, the existing algorithms to solve the path problem can only find a single optimal path, cannot satisfactorily find multiple groups of optimal solutions at the same time, and it is very necessary to propose as many solutions as possible. So this paper carries out a research on the Multi-modal Multi-Objective Path Planning (MMOPP), the objective is to find all sets of Pareto optimal path solutions from the start point to the end point in a grid map. This paper proposes a multi-modal multi-objective ant colony path planning optimization algorithm based on matrix preprocessing technology and Dijkstra algorithm (MD-ACO). Firstly, a new method of storing maps that reduces the size of the map and reduces the size of the decision space has been proposed in this paper. Secondly, using the characteristics of the Dijkstra algorithm that can quickly find the optimal path, generate an initial feasible solution about the problem, and improve the problem that the initial pheromone of ant colony algorithm is insufficient and searching for solutions is slow. Thirdly, a reasonable threshold is set for the pheromone to avoid algorithm getting stuck in local optimal solution. Finally, the algorithm is tested on the MMOPP test sets to evaluate the performance of the algorithm, and the results show that MD-ACO algorithm can solve MMOPP and get the optimal solution set. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ant colony optimization

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Effects of External Archives on the Performance of Multi-Objective Evolutionary Algorithms on Real-World Problems

Effects of External Archives on the Performance of Multi-Obj...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Nan, Yang Shu, Tianye Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350314588

External archives have attracted more and more attention in the evolutionary multi-objective optimization (EMO) community. This is because a solution set selected from an external archive is usually better than the final population of an EMO algorithm. Whereas the effects of subset selection from external archives have already been investigated on artificial test problems, its effects on real-world problems have not been examined. In this paper, we examine the effects of subset selection from external archives for ten EMO algorithms on two real-world problem suites. Experimental results show that the performance improvement by subset selection is large for most algorithms and many problems but small for a few algorithms and a few problems (i.e., algorithm dependent and problem dependent). © 2023 IEEE.

关键词： Multiobjective optimization

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