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Cloud Resource Scheduling Using Multi-Strategy Fused honey badger algorithm

BIG DATA 2025年第1期13卷 59-72页

作者： Xie, Haitao Li, Chengkai Ye, Zhiwei Zhao, Tao Xu, Hui Du, Jiangyi Bai, Wanfang Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Hubei Univ Technol Sch Sci Wuhan Peoples R China Xining Big Data Serv Adm Xining Peoples R China

Cloud resource scheduling is one of the most significant tasks in the field of big data, which is a combinatorial optimization problem in essence. Scheduling strategies based on meta-heuristic algorithms (MAs) are often chosen to deal with this topic. However, MAs are prone to falling into local optima leading to decreasing quality of the allocation scheme. algorithms with good global search ability are needed to map available cloud resources to the requirements of the task. honey badger algorithm (HBA) is a newly proposed algorithm with strong search ability. In order to further improve scheduling performance, an Improved honey badger algorithm (IHBA), which combines two local search strategies and a new fitness function, is proposed in this article. IHBA is compared with 6 MAs in four scale load tasks. The comparative simulation results obtained reveal that the proposed algorithm performs better than other algorithms involved in the article. IHBA enhances the diversity of algorithm populations, expands the individual's random search range, and prevents the algorithm from falling into local optima while effectively achieving resource load balancing.

关键词： honey badger algorithm big data cloud resource scheduling analytics and visualization load balancing

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Dynamic path planning for spacecraft rendezvous and approach based on hybrid honey badger algorithm

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JOURNAL OF THE FRANKLIN INSTITUTE 2025年第1期362卷

作者： Huang, Cheng Wang, Tao Wang, Shuaikang Xu, Jiazhong Harbin Univ Sci & Technol Sch Automat Heilongjiang Prov Key Lab Complex Intelligent Syst Harbin 150080 Peoples R China

In order to solve the 3D path planning of the close-range guidance phase of spacecraft in space rendezvous and approaching, and improve its optimization performance in dynamic environments, a 3D dynamic path planning method based on hybrid honey badger algorithm and cubic spline interpolation is proposed. Firstly, a hybrid honey badger algorithm is presented for dynamic path planning: the chaotic reverse learning is used to initialize the population, so as to improve the global search ability;the transfer operators are introduced in the mode transition stage to balance exploration and exploitation capabilities;the sine and cosine operators are integrated during the stage of location update to improve the quality of the optimal solution;an adaptive disturbance coefficient is introduced into the mining mode to accelerate the convergence. Secondly, the cubic spline interpolation algorithm is combined to optimize the path curve and reduce the turning angle of the curve. Finally, the comparative simulation experiments verify that the proposed method can plan a smooth path without collision. The planned path length is relatively shortest, the path turning angle and the algorithm running time are relatively smallest.

关键词： honey badger algorithm Dynamic path planning Chaotic reverse learning Spline interpolation optimization

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Energy-Aware Routing in WSN Utilizing Self-Attention-Based Cycle-Consistent Generative Adversarial Network With honey badger algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第5期38卷

作者： bhaskar, K. Vijaya Jyothi, A. P. Chandrasekaran, Sivasankar Thangathurai, Vignesh Saveetha Univ Saveetha Inst Med & Tech Sci SIMATS Saveetha Sch Engn Dept Comp Sci & Engn Chennai 602105 Tamil Nadu India M S Ramaiah Univ Appl Sci Fac Engn & Technol Dept Comp Sci & Engn Bengaluru Karnataka India Panimalar Engn Coll Dept Comp Sci & Business Syst Chennai Tamil Nadu India

Energy efficiency and secure data transmission are considered as the main design targets of wireless sensor network (WSN). Previous energy-aware cluster head (CH) selection approaches could not provide secured data transmission. To address this problem, a self-attention-based cycle-consistent generative adversarial network (SaCyCsGAN) with honey badger algorithm (HyBA)-adopted energy-aware routing (EgAR) in WSN is proposed. Initially, the proposed system uses a CH to carry out the routing practice. Accordingly, SaCyCsGAN classifier is exploited for CH selection under firm fitness function: delay, distance, energy, cluster density, and traffic rate. Afterward CH selection, a spiteful node may enter the cluster and acts as a CH. Hence, best path selection is needed. For that, HyBA is utilized, it selects the best path depending upon triplet parameter: trust, connectivity, and service degree. Finally, data are conveying into base station (BS) and vice versa under best path. Finally, the proposed EgAR-WSN-SaCyCsGAN-HyBA method attains 24.13%, 28.57%, 5.88%, and 20.37% higher throughput and 18.50%, 21.67%, 13.33%, and 22.22% lesser energy consumption evaluated to the existing methods such as multiple-objective CH utilizing self-attention basis progressive generative adversarial network for safe data aggregation (EgAR-WSN-SAPrGAN-ArVOA), reinforcement learning-based energy-efficient optimized routing protocol in WSN (EgAR-WSN-RLGT-BCSA), data aggregation including clustering protocol in WSN under machine learning (EgAR-WSN-AfNN), and optimum cluster along trusted path for routing formation with categorization of intrusion under machine learning categorization (EgAR-WSN-RsBPDT-HoCSOA).

关键词： cluster head selection energy-aware routing epitome path selection honey badger algorithm

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Multi-Peak Photovoltaic Maximum Power Point Tracking Method Based on honey badger algorithm Under Localized Shading Conditions

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ENERGIES 2025年第5期18卷 1258-1258页

作者： Gui, Qianjin Wang, Lei Ding, Chao Xu, Wenfa Li, Xiaoyang Yu, Feilong Wang, Haisen State Grid Anqing Elect Power Supply Co Anqing 246000 Peoples R China State Grid Anhui Elect Power Co Ltd Hefei 230000 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

The P-V and I-V curves of photovoltaic (PV) strings show multiple peaks when exposed to partial shading conditions (PSCs). The traditional maximum power point tracking (MPPT) method cannot track the global maximum power point (GMPP) due to the multi-peak characteristics, power fluctuation, and tracking speed. In this paper, a multi-peak PV MPPT method based on the honey badger algorithm (HBA) is proposed to track the GMPP in a localized shading environment. The performance of this method is also compared and analyzed with the traditional MPPT methods based on the perturbation observation (P&O) method and Particle Swarm Optimization (PSO) algorithm. The experimental results have proven that, compared with the MPPT methods based on P&O and PSO, the proposed multi-peak MPPT method based on the HBA algorithm has a faster tracking speed, higher tracking accuracy, and fewer iterations.

关键词： photovoltaic power systems partial shading multi-peak power points honey badger algorithm maximum power point tracking

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Symbiotic mechanism-based honey badger algorithm for continuous optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-26页

作者： Xu, Yuefeng Zhong, Rui Cao, Yang Zhang, Chao Yu, Jun Niigata Univ Grad Sch Sci & Technol Niigata Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Japan Univ Toyama Fac Engn Toyama Japan Niigata Univ Inst Sci & Technol Niigata Japan

honey badger algorithm (HBA) is a novel meta-heuristic algorithm inspired by the foraging behaviors of honey badgers, designed to tackle single-objective continuous optimization problems. While the standard HBA demonstrates strong performance across various optimization problems, it still encounters challenges related to insufficient population diversity, particularly in later stages, hindering its ability to escape local optima. To address these problems, we propose an enhanced variant called the symbiotic mechanism-based HBA (SHBA), which incorporates the cooperative symbiotic mechanism between honey badgers and honey-guide birds. Specifically, it integrates information exchange between two distinct populations and employs multiple strategies to increase population diversity while maintaining efficient search performance. We conducted numerical experiments on CEC2017 and CEC2022 and compared their performance with nine competitive algorithms. Furthermore, the scalability and applicability of the proposed SHBA were assessed across various engineering problems. The statistical results show that the proposed SHBA is significantly competitive, with excellent convergence speed and accuracy in benchmark functions and engineering problems.

关键词： Heuristic optimization algorithm Evolutionary computation honey badger algorithm Symbiotic mechanism

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An improved multi-objective honey badger algorithm based on global searching strategy

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JOURNAL OF SUPERCOMPUTING 2025年第5期81卷 1-37页

作者： Cui, Jiarui Zhou, Hao Yan, Qun Huang, Jian Wang, Minggang Yang, Xu Li, Qing Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Zunyi Aluminum Stock Corp Ltd Zunyi 563100 Peoples R China

This paper proposes an improved version of the honey badger algorithm (HBA) for multi-objective optimization problems, referred to as the Improved Multi-Objective honey badger algorithm (IMOHBA). The collective behavior search strategy of the HBA is integrated with a dynamic archive to efficiently retrieve and store Pareto optimal solutions. Additionally, a leader selection mechanism based on crowding distance and the roulette wheel strategy is employed to select the optimal solution in multi-objective space. To overcome the issue of local optima, a modified mutualism phase from the Symbiotic Organisms Search (SOS) algorithm is introduced to enhance the global search capability. The algorithm is tested on CEC2009 benchmark functions and various real-world engineering problems, demonstrating competitive performance for solving complex multi-objective optimization problems.

关键词： Multi-objective optimization honey badger algorithm Global search Symbiotic organisms search

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A Hybrid honey badger algorithm to Solve Energy-Efficient Hybrid Flow Shop Scheduling Problems

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PROCESSES 2025年第1期13卷 174-174页

作者： Geetha, M. Sekar, R. Chandra Guru Marichelvam, M. K. Kamaraj Coll Engn & Technol Dept Math SPG Chidambara Nadar Vellakulam 625701 Tamilnadu India Mepco Schlenk Engn Coll Dept Math Sivakasi 626005 Tamilnadu India Mepco Schlenk Engn Coll Dept Mech Engn Sivakasi 626005 Tamilnadu India

A well-planned schedule is essential to any organization's growth. Thus, it is important for the literature to cover a more comprehensive range of scheduling problems. In this paper, energy-efficient hybrid flow shop (EEHFS) scheduling problems are considered. Researchers have developed several techniques to deal with EEHFS scheduling problems. Also, researchers have recently proposed several metaheuristics. honey badger algorithm (HBA) is one of the most recent algorithms proposed to solve various optimization problems. The objective of the present work is to solve EEHFS scheduling problems using the Hybrid honey badger algorithm (HHBA) to reduce the makespan (Cmax) and total energy cost (TEC). In the HHBA, a constructive heuristic known as the NEH heuristic was incorporated with the honey badger algorithm. The suggested algorithm's performance was verified using an actual industrial scheduling problem. The company's results are compared with those of the HHBA. The HHBA could potentially result in an 8% decrease in total energy cost. Then, the proposed algorithm was applied to solve 54 random benchmark problems. The results of the proposed HHBA were compared with the FIFO dispatching rule, the NEH heuristic, and other metaheuristics such as the simulated annealing (SA) algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, honey badger algorithm, and the Ant Colony Optimization (ACO) algorithms addressed in the literature. Average percentage deviation (APD) was the performance measure used to compare different algorithms. The APD of the proposed HHBA was zero. This indicates that the proposed HHBA is more effective in solving EEHFS scheduling problems.

关键词： scheduling honey badger algorithm hybrid flow shop energy-efficient makespan (Cmax) total energy cost

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Flexible operation sequence planning optimization for machined parts based on application of a discrete honey badger algorithm and a hybrid Artificial Neural Network-honey badger algorithm

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2025年

作者： Behera, Gobinda Chandra Deb, Sankha Indian Inst Technol Kharagpur Dept Mech Engn FMS & CIM Lab Kharagpur 721302 India

Process planning determines most efficient and economical way of part manufacturing satisfying resource and technological constraints. Operation sequencing determines optimal order of machining operations by minimizing time, cost and ensuring product quality. It is NP-hard owing to computational complexities associated with search space, making solution by exact mathematical models challenging. Here, we propose honey badger algorithm(HBA) and its hybrid with Artificial Neural Network(ANN) with the objective to generate multiple optimal sequences by minimizing total cost associated with nonproductive tasks like changes in machines, tools and Tool Approach Direction(TADs) subject to precedence constraints. Flexibilities in machines, tools and TADs are considered as they are crucial for adapting to dynamic manufacturing environments. We modified basic HBA to suit discrete problem nature since originally developed HBA was meant for continuous optimization problems. Comparisons with other well-established algorithms demonstrated superior performance of HBA-based algorithms in terms of cost, computational time, convergence rate and robustness.

关键词： Optimization operation sequencing process planning honey badger algorithm hybrid ANN-HBA

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Fault Diagnosis of Photovoltaic Array Based on Improved honey badger Optimization algorithm

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ENERGIES 2025年第4期18卷 841-841页

作者： Guo, Zhuo Chang, Yuanyuan Fang, Yanhong Liaoning Tech Univ Fac Elect & Control Engn Fuxin 123000 Peoples R China Liaoning Finance & Trade Coll Sch Econ & Management Huludao 125105 Peoples R China

A photovoltaic array fault diagnosis method based on an improved honey badger optimization algorithm is proposed to improve the accuracy of photovoltaic array fault diagnosis. Firstly, analyze the current and power output characteristic curves of the photovoltaic array under different states, and construct a preliminary set of 10 dimensional fault feature vectors. Secondly, the feature vector set is ranked in importance using the random forest algorithm, and then input into support vector machines, long short-term memory, and bidirectional long short-term memory neural networks to obtain the optimal combination of the base model and the number of features. Then, the honey badger optimization algorithm was improved by combining Tent chaotic mapping column measurement, improved control factors, and pinhole imaging strategy, and compared with other optimization algorithms to demonstrate its effectiveness in optimization ability, stability, and convergence speed. Finally, by combining the improved honey badger optimization algorithm with the optimal base model and number of features, the problem of hyperparameter setting in the base model was effectively solved. The experimental results show that the fault diagnosis accuracy of the proposed photovoltaic array fault diagnosis model is 97.1014%, which is superior to other models and verifies the effectiveness of the proposed method.

关键词： photovoltaic array fault diagnosis feature extraction random forest honey badger algorithm

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honey badger algorithm using lens opposition based learning and local search algorithm

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EVOLVING SYSTEMS 2024年第2期15卷 335-360页

作者： Majumdar, Parijata Mitra, Sanjoy Bhattacharya, Diptendu Natl Inst Technol Agartala 799046 West Tripura India Tripura Inst Technol Agartala 799009 West Tripura India

honey badger algorithm (HBA) is a recently developed metaheuristic algorithm inspired by the intelligent foraging behavior of honey badgers where its digging and honey-finding strategies are simulated in the exploration and exploitation of the search space. However, HBA may suffer from slow convergence speed, unbalanced exploration and exploitation, and local optima stagnation problems. To overcome these limitations, we proposed a modified variant of HBA (LOLSHBA) where Lens Opposition Based Learning is used at the initialization phase to enhance the population quality and diversity and better exploration. The exponential decay function is used as a density factor for accelerating the global convergence speed and for controlling the smooth time-varying transition from exploration to the exploitation phase. A time-varying acceleration constant is also incorporated in the density factor to maintain the balance of local searching and global convergence by emulating a natural reflex action in the movement of honey badgers and guiding the hunting process in each iteration. Finally, to increase exploitation, the Local Search algorithm is used to boost the search in the local neighborhood of the global best solution at the end of every iteration. For performance evaluation, we utilized well-known benchmark functions where the proposed LOLSHBA performs better in 12 out of 19 functions and 26 out of 29 IEEE CEC 2017 functions. Statistical analyses also validate the superior performance of LOLSHBA. Besides, LOLSHBA is also tested for problem-solving which proves that LOLSHBA is more suitable to handle constrained engineering design problems compared to other optimization algorithms.

关键词： honey badger algorithm Lens opposition based learning Exponential decay function Time varying acceleration constant Local search algorithm

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