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harris hawk optimization algorithm Based on Cauchy Distribution Inverse Cumulative Function and Tangent Flight Operator

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APPLIED INTELLIGENCE 2022年第10期52卷 10999-11026页

作者： Wang, Min Wang, Jie-Sheng Li, Xu-Dong Zhang, Min Hao, Wen-Kuo Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

harris hawk optimization (HHO) algorithm is a new population-based and nature-inspired optimization paradigm, which has strong global search ability, but its diversified local search strategies easily make it fall into local optimum. In order to enhance its search mechanism and speed of convergence, an new improved HHO algorithm based on the inverse cumulative function operator of Cauchy distribution and tangent flight operator was proposed. The proposed two operators are used as scale factors to control the step size. The walk path of Cauchy inverse cumulative integral function shows that its trajectory step length is relative to the average, which can further enhance the search stability of the algorithm. The Tangent flight has the function of balanced exploitation and exploration, and enhances the convergence ability of the algorithm. In order to verify the performance of the proposed algorithm, the 30 benchmark functions of the 2017 Institute of Electrical and Electronic Engineers (IEEE) Conference on Evolutionary Computation (CEC2017) and two practical engineering design problems are adopted to carry out the simulation experiments. On the other hand, the covariance matrix adaptation evolutionary strategies (CMA-ES), arithmetic optimization algorithm (AOA), butterfly optimization algorithm (BOA), bat algorithm (BA), whale optimization algorithm (WOA), sine cosine algorithm (SCA), and the proposed HHO algorithms were used for comparison experiments. Simulation results show that the proposed the Cauchy-distribution and Tangent-Flight harris hawk optimization (CTHHO) algorithm has strong optimization capability.

关键词： harris hawk optimization algorithm Cauchy distribution Inverse cumulative function Tangent flight operator Function optimization Engineering optimization

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Improved task scheduling in heterogeneous distributed systems using intelligent greedy harris hawk optimization algorithm

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EVOLUTIONARY INTELLIGENCE 2024年第5-6期17卷 4199-4226页

作者： Roudsari, Mohammad Navid Habibpour Islamic Azad Univ Dept Comp Engn Najafabad Branch Najafabad Iran

In the context of heterogeneous computer systems, the utilization of task scheduling algorithms is crucial for enhancing system performance. A major challenge in the development of efficient scheduling algorithms for such systems lies in reducing execution time. However, achieving this objective is not a straightforward process. Due to the complexity of these problems, exact algorithms are not feasible for solving them, primarily because of the vast search space involved. Consequently, approximate algorithms become necessary. Among these, meta-heuristic algorithms are notable for their ability to explore large search spaces and generate suitable solutions. Nevertheless, these algorithms are not flawless as they may become trapped in local optima and failing to scan the entire sample space. To address task scheduling problems in heterogeneous computer systems, this research proposes the Intelligent harris hawk optimization algorithm. The algorithm was employed to tackle the task scheduling problem in heterogeneous systems, while also considering common issues associated with such algorithms, including early convergence and limited exploration capability during the exploration phase. To overcome these challenges, the exploration phase was separated from the exploitation phase. This approach aimed to ensure that the algorithm explores the entire problem space without necessarily achieving optimality. However, if the algorithm becomes trapped in a local optimum during the exploitation phase, it may not be able to escape and achieve early convergence. To address this issue, a solution called Bigmax to Fitmax was proposed for escaping local optima in the soft besiege phase, while a unique technique called Crisscross Mutation was introduced for the hard besiege phase. In the final stage, the proposed algorithm will be compared with several other algorithms, including HHO (harris hawk optimization), MOSCOA (A multi-objectives scheduling algorithm based on cuckoo optimizatio

关键词： Task scheduling Heterogeneous systems harris hawk optimization algorithm Meta-heuristic algorithm

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Adaptive Variable Universe Fuzzy Droop Control Based on a Novel Multi-Strategy harris hawk optimization algorithm for a Direct Current Microgrid with Hybrid Energy Storage

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ENERGIES 2024年第21期17卷 5296页

作者： Wang, Chen Jiao, Shangbin Zhang, Youmin Wang, Xiaohui Li, Yujun Xian Univ Technol Shaanxi Key Lab Complex Syst Control & Intelligent Xian 710048 Peoples R China Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ H3G 1M8 Canada Xian Thermal Power Res Inst Co Ltd Xian 710054 Peoples R China

In the off-grid photovoltaic DC microgrid, traditional droop control encounters challenges in effectively adjusting the droop coefficient in response to varying power fluctuation frequencies, which can be influenced by factors such as line impedance. This paper introduces a novel Multi-strategy harris hawk optimization algorithm (MHHO) that integrates variable universe fuzzy control theory with droop control to develop an adaptive variable universe fuzzy droop control strategy. The algorithm employs Fuch mapping to evenly distribute the initial population across the solution space and incorporates logarithmic spiral and improved adaptive weight strategies during both the exploration and exploitation phases, enhancing its ability to escape local optima. A comparative analysis against five classical meta-heuristic algorithms on the CEC2017 benchmarks demonstrates the superior performance of the proposed algorithm. Ultimately, the adaptive variable universe fuzzy droop control based on MHHO dynamically optimizes the droop coefficient to mitigate the negative impact of internal system factors and achieve a balanced power distribution between the battery and super-capacitor in the DC microgrid. Through MATLAB/Simulink simulations, it is demonstrated that the proposed adaptive variable universe fuzzy droop control strategy based on MHHO can limit the fluctuation range of bus voltage within +/- 0.75%, enhance the robustness and stability of the system, and optimize the charge and discharge performance of the energy storage unit.

关键词： photovoltaic DC microgrid adaptive variable universe fuzzy control droop control harris hawk optimization algorithm

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An Improved harris hawk optimization algorithm

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Mechanical Engineering Science 2024年第1期6卷 21-25页

作者： GuangYa Chong Yongliang YUAN He’nan Polytechnic University JiaozuoHe’nan454150China

Aiming at the problems that the original harris hawk optimization algorithm is easy to fall into local optimum and slow in finding the optimum,this paper proposes an improved harris hawk optimization algorithm(GHHO).Firstly,we used a Gaussian chaotic mapping strategy to initialize the positions of individuals in the population,which enriches the initial individual species ***,by optimizing the energy parameter and introducing the cosine strategy,the algorithm's ability to jump out of the local optimum is enhanced,which improves the performance of the ***,comparison experiments with other intelligent algorithms were conducted on 13 classical test function *** results show that GHHO has better performance in all aspects compared to other optimization *** improved algorithm is more suitable for generalization to real optimization problems.

关键词： harris hawk optimization algorithm chaotic mapping cosine strategy function optimization

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An Improved Feature Selection algorithm for harris hawk optimization Based on Hybrid Strategy 6

An Improved Feature Selection Algorithm for Harris Hawk opti...

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6th International Conference on Robot Systems and Applications (ICRSA)

作者： Shi, Zhanyi Yi, Guohong Wuhan Inst Technol Hubei Key Lab Intelligent Robot Sch Comp Sci & Engn Wuhan 430205 Hubei Peoples R China

ISBN: (纸本)9798400708039

Feature selection is a popular issue in machine learning, which involves selecting the most effective features from the original features to reduce the dimensionality of the dataset. It is an important means to improve the performance of learning algorithms and a key data preprocessing step in pattern recognition. An improved feature selection algorithm for harris hawk optimization (HHO) is proposed to address the shortcomings of the algorithm. First, self-adaptive elite opposition-based learning strategy is introduced in the exploration phase to initialize the position of harris hawk population. Secondly, using logarithmic inertia weight to improve the update formula of escape energy, introducing the number of iterations into the jump distance, adjusting the search distance of HHO using step size adjustment parameters, and balancing exploration and development capabilities;On this basis, an improved HHO algorithm was designed to avoid the HHO algorithm falling into local optima. Then, a quadratic transfer function was introduced to update the binary and population positions of the improved HHO algorithm, and an IHHO algorithm was designed to solve feature selection problems. Experiments were conducted on five common benchmark datasets including Wine, Segment, Spectfheart, Sonar, and Ionosphere, and the results showed that the improved algorithm can effectively improve classification accuracy and select the optimal features.

关键词： Feature selection harris hawk optimization algorithm Fitness function Swarm intelligence Binary

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A deep hybrid prediction framework for building operational carbon emissions: Integrating enhanced extreme learning machines

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ENERGY REPORTS 2025年 13卷 4126-4140页

作者： Wang, Jingqi Tan, Yongguang Yu, Junqi Yu, Haifeng Wang, Meng Zhou, Meng Xian Univ Architecture & Technol Sch Bldg Serv Sci & Engn Xian 710055 Peoples R China Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China PipeChina Beijing Pipeline Co Ltd Beijing 100083 Peoples R China Xian Univ Architecture & Technol Sch Management Xian 710055 Peoples R China

To address the urgent need for reducing carbon emissions in the construction industry, this paper proposes a novel hybrid prediction model, MIV-IHHO-DELM, for forecasting carbon emissions from public building operations. Unlike traditional single prediction models, which often lack accuracy and stability, our model integrates three key innovations: (1) an Improved harris hawk optimization algorithm (IHHO) enhanced with three strategies to improve high-dimensional optimization capabilities;(2) a Deep Extreme Learning Machine (DELM) optimized by IHHO for initial weight tuning;and (3) the Mean Impact Value (MIV) method for feature selection, which effectively screens multiple parameters from three categories: building energy structure and usage habits, outdoor meteorological conditions, and historical carbon emission *** extensive experimental analysis, the proposed MIV-IHHO-DELM model demonstrates superior performance. In ablation experiments, the model achieves a 46.26 % reduction in MAPE and a 37.92 % reduction in RMSE compared to the IHHO-DELM model, and a 71.62 % reduction in MAPE and 73.74 % reduction in RMSE compared to the MIV-DELM model. In comparative experiments, the MIV-IHHO-DELM model achieves a MAPE of 0.704 % and RMSE of 18.0133, outperforming other state-of-the-art prediction models in both accuracy and computational efficiency. Furthermore, the model exhibits strong generalization ability, maintaining high prediction accuracy even with reduced data *** study contributes to the field by providing a robust and efficient tool for predicting carbon emissions in public buildings, offering valuable data support for carbon reduction strategies in the construction sector.

关键词： Public buildings Carbon emission prediction Deep learning Deep extreme learning machine harris hawk optimization algorithm

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Greedy mechanism-based bi-objective optimization for green scheduling in manufacturing systems considering transportation

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APPLIED SOFT COMPUTING 2025年 175卷

作者： Wang, Zhu Qiu, Rongping Zhou, Binghai Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China Tongji Univ Inst Ind Engn Sch Mech Engn Shanghai 201804 Peoples R China

This paper addresses scheduling challenges in hybrid flow manufacturing systems with crane transportation (HFMS-CT) driven by intelligent control, mass customization, and eco-friendly manufacturing. Unlike previous studies, it considers the interdependence between machine processing and crane transport, focusing on minimizing both makespan and energy consumption. A bi-objective mixed-integer programming model is developed, and the Epsilon-constraint method is used for small-scale cases. Given the NP-hardness, a modified multi-objective harris hawk optimization (MMOHHO) is proposed. It adopts greedy mechanisms by integrating Laplace crossover, tent-based chaotic mapping, elite selection, and nonlinear optimization strategy to balance exploration and exploitation capabilities. The proposed algorithm is compared with the Epsilon-constraint method and benchmark metaheuristics. The experimental results reveal that the proposed algorithm outperforms other methods regarding NPS, DPO, IGD, and ES evaluation metrics. Finally, an in-depth discussion is conducted using a real-world case study, offering valuable managerial insights and practical recommendations for implementation.

关键词： Hybrid flow manufacturing systems with crane transportation Green scheduling Conflict analysis harris hawk optimization algorithm Epsilon-constraint

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Classifying victim degree of injury in road traffic accidents: a novel stacked DCL-X approach

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第25期83卷 66691-66723页

作者： Altaf, Insha Kaul, Ajay Univ Kashmir CSE Srinagar Jammu & Kashmir India Shri Mata Vaishno Devi Univ Katra India

Road Traffic Injuries are one of the world's leading cause of death, with greatest burden falling on nations with lower and moderate incomes. They are consistently ranked in top 10 leading causes of mortality worldwide for persons of all ages. The biggest advantage of classifying victim degree of injuries in road accidents can pave a way for safer roads and reduced accident rates. This article employs California based SWITRS dataset to propose a novel approach namely Stacked DCL-X model for classifying ``victim_degree_of_injury ''\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$``victim\_degree\_of\_injury''$$\end{document}. It classifies injuries that might take place due to collisions occurring between vehicles and near by pedestrians, obstacles etc. on roads. To verify the superiority of our proposed model, several Machine Learning algorithm-based classification models are stacked together to classify ``victim_degree_of_injury ''\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$``victim\_degree\_of\_injury''$$\end{document}. A total of 1 27 000 accidents are considered from SWITRS dataset when determining the ``victim_degree_of_injury ''\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$``victim\_degree\_of\_injury''$$\end{document}. Machine Learning classifiers implemented in this article includes XGBoost, CatBoost, LightGBM, Decision Tree, Random Forest, Gradient Boosting and Stacked DCL-X. In addition, the algorithm used at featur

关键词： Victim degree of injury Nature inspired algorithm harris hawk optimization algorithm Stacking Stacked DCL-X algorithm

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Task scheduling approach in fog and cloud computing using Jellyfish Search (JS) optimizer and Improved harris hawks optimization (IHHO) algorithm enhanced by deep learning

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第7期27卷 8939-8963页

作者： Jafari, Zahra Navin, Ahmad Habibizad Zamanifar, Azadeh Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran

Scheduling tasks is a pivotal and practical action within the fog and cloud layers. This study introduces a two-tier task scheduling approach with improved version of the harris hawk optimization algorithm to lower delay and power consumption. We employ the ConvLSTM neural network in fog layer to predict the optimal location for task execution and estimate the workload of virtual machines. We harness the Jellyfish Search (JS) optimizer to schedule executable tasks within the fog layer efficiently. Furthermore, we present an enhanced version of the harris hawk optimization algorithm, incorporating sine and cosine searching for task scheduling optimization. Our proposed algorithm can predict virtual machine workloads and task execution locations based on task and resource characteristics, resulting in reduced task completion times and energy consumption. Our experiments demonstrate that our approach exhibits lower delays and energy consumption in cloud layer task scheduler compared to the MGWO, NSGA-II, and MOPSO algorithms. Furthermore, it outperforms several algorithms, including CGO, AOS, CSA, WOA, HGSWC, MPA, and CHMPAD, when it comes to minimizing delays in fog layer task scheduling, ultimately leading to faster task execution.

关键词： Task scheduling Fog computing Cloud computing Jellyfish Search (JS) optimizer algorithm harris hawk optimization algorithm Deep learning

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Optimal Path Planning algorithm of Indoor AGV Enhanced with HHO and Sector-Wide Field DWA

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UNMANNED SYSTEMS 2025年第0期

作者： Zhou, Zhen Song, Yujie Li, Shuo Hebei Univ Coll Elect & Informat Engn Key Lab Digital Med Engn Hebei Prov Baoding 071000 Peoples R China

With an aim to solve the issue of dynamic obstacles and the global optimal path planning for indoor Automated Guided Vehicles (AGVs), this paper addressed an enhanced hybrid path planning method based on the improvement of harris hawk optimization (HHO) and Dynamic Window Approach (DWA). The main contributions are given as follows. On the one hand, compared to the traditional HHO, the performances are improved in the following aspects. First, initializing the harris hawk population with a circle-tent chaotic map to enhance search capability. Second, balancing global and local searches with a segmented strategy to avoid local optima;Third, improving population survival with a hard-constraint mutation strategy. On the other hand, DWA is enhanced from the following three perspectives: a sector potential field is adopted for early obstacle avoidance and a smoother AGV trajectories;a dynamic weighting strategy is used to obtain a better escape local optima and enhance safety;a obstacle-free path attraction strategy is proposed to solve the problem of detours and accelerates the convergence speed. Finally, combined the enhanced HHO with DWA, Generalized harris hawks optimization-Dynamic Window Approach (GHHO-DWA) algorithm addresses the issues of nonsmooth global paths and inadequate dynamic obstacle avoidance. Meanwhile, the effectiveness and practicality of GHHO-DWA algorithm are validated through MATLAB simulations and physical testing on the QBot2e platform.

关键词： harris hawk optimization algorithm path planning dynamic path planning sector-wide field

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