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Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing

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Journal of Bionic Engineering 2023年第5期20卷 2240-2275页

作者： Jiaochen Chen Zhennao Cai Huiling Chen Xiaowei Chen José Escorcia-Gutierrez Romany F.Mansour Mahmoud Ragab College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Department of Rheumatology and Immunology The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China Department of Computational Science and Electronics Universidad de la CostaCUC080002BarranquillaColombia Department of Mathematics Faculty of ScienceNew Valley University72511El-KhargaEgypt Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing *** assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN *** method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS *** DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 *** addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological *** results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal *** to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation *** research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.

关键词： Multi-threshold image segmentation 2D Rényi entropy Renal pathology Cuckoo search algorithm swarm intelligence algorithms Bionic algorithm

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Self-adaptive teaching-learning-based optimizer with improved RBF and sparse autoencoder for high-dimensional problems ?

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INFORMATION SCIENCES 2023年 630卷 463-481页

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Zhou, MengChu Beijing Univ Technol Sch Software Engn Fac Informat Technol Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Southern Methodist Univ Dept Comp Sci Lyle Sch Engn Dallas TX 75205 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

Evolutionary algorithms and swarm intelligence ones are commonly used to solve many complex optimization problems in different fields. Yet, some of them have limited performance when dealing with high-dimensional complex problems because they often require enormous computational resources to yield desired solutions, and some of them may easily trap into local optima. To solve this problem, this work proposes a Self-adaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA). In STORA, a Self-adaptive Teaching-Learning-Based Optimizer (STLBO) is designed to dynamically adjust parameters for balancing exploration and exploitation abilities. Then, a sparse autoencoder (SAE) is adopted as a dimension reduction method to compress a search space into a lower-dimensional one for more efficiently guiding a population to converge towards global optima. Besides, an Improved Radial Basis Function model (IRBF) is designed as a surrogate one to balance training time and prediction accuracy. It is adopted to save computational resources for improving overall performance. In addition, a dynamic population allocation strategy is adopted to well integrate SAE and IRBF in STORA. We evaluate STORA by comparing it with several state-of-the-art algorithms through eight benchmark functions. We further test its actual performance by applying it to solve a real-world computation offloading problem.

关键词： Evolutionary algorithms swarm intelligence algorithms Teaching-learning-based optimizer Autoencoders Radial basis function model

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algorithms for path optimizations: a short survey

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COMPUTING 2023年第2期105卷 293-319页

作者： De Sirisuriya, S. C. M. S. Fernando, T. G., I Ariyaratne, M. K. A. Gen Sir John Kotelawala Def Univ Fac Comp Dept Comp Sci Rathmalana Sri Lanka Univ Sri Jayewardenepura Fac Grad Studies Nugegoda Sri Lanka Univ Sri Jayewardenepura Fac Appl Sci Dept Comp Sci Nugegoda Sri Lanka

Path finding is used to solve the problem of finding a traversable path through an environment with obstacles. This problem can be seen in many different fields of study and these areas rely on fast and efficient path finding algorithms. This paper aims to describe and review state of the art optimization techniques that are used on optimized path finding and compare their performances. Moreover, a special attention is paid on the proposed approaches to identify how they are tested on different test cases;whether the test cases are automatically generated or benchmark instances. The review opens avenues about the importance of automatic test case generation to test the different path finding algorithms.

关键词： Path Optimization Evolutionary algorithms swarm intelligence algorithms Machine Learning algorithms Robotic Path Planing Vehicle Routing

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Cauchy mutation boosted Harris hawk algorithm: optimal performance design and engineering applications

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第2期10卷 503-526页

作者： Shan, Weifeng He, Xinxin Liu, Haijun Heidari, Ali Asghar Wang, Maofa Cai, Zhennao Chen, Huiling Inst Disaster Prevent Sch Emergency Management Langfang 065201 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

Harris hawks optimization (HHO) has been accepted as one of the well-established swarm-based methods in the community of optimization and machine learning that primarily works based on multiple dynamic features and various exploratory and exploitative traits. Compared with other optimization algorithms, it has been observed that HHO can obtain high-quality solutions for continuous and constrained complex and real-world problems. While there is a wide variety of strategies in the HHO for dealing with diverse situations, there are chances for sluggish performance, where the convergence rate can gradually slow with time, and the HHO may stay stuck in the current relatively better place and may be unable to explore other better areas. To mitigate this concern, this paper combines the Cauchy mutation mechanism into the HHO algorithm named CMHHO. This idea can boost performance and provide a promising optimizer for solving complex optimization problems. The Cauchy mutation mechanism can speed up the convergence of the solution and help HHO explore more promising regions compared to its basic release. On 30 IEEE CEC2017 benchmark functions, the study compared the proposed CMHHO with various conventional and advanced metaheuristics to validate its performance and quality of solutions. It has been found through experiments that the overall optimization performance of CMHHO is far superior to all competitors. The CMHHO method is applied to four engineering challenges to investigate the capabilities of the proposed algorithm in solving real-world problems, and experimental results show that the suggested algorithm is more successful than existing algorithms.

关键词： Harris hawks optimization Cauchy mechanism swarm intelligence algorithms engineering problems

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Intelligent mix design of recycled brick aggregate concrete based on swarm intelligence

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JOURNAL OF BUILDING ENGINEERING 2023年 71卷

作者： Wang, Shiqi Xia, Peng Wang, Zhao Meng, Tao Gong, Fuyuan Zhejiang Univ Coll Civil Engn & Architecture Hangzhou Peoples R China Yokohama Natl Univ Inst Urban Innovat Yokohama Japan

The mechanical properties of recycled brick aggregate concrete (RBAC) are significantly affected by design parameters such as recycled brick aggregate (RBA) replacement ratio and water-cement ratio, which not only increases the difficulty of designing RBAC by experimental or theoretical methods, but also makes the mechanical properties (compressive strength, flexural strength, etc.) of RBAC difficult to match the design requirements. Therefore, an intelligent mix design method for RBAC is proposed in this paper, which can determine the best RBAC mix proportion design scheme according to the design parameters of RBAC (cement content, water-cement ratio and crushed brick ratio, etc.). This method is compared with the performance of 8 machine learning algorithms by creating a database containing 8 input variables. The most suitable model for predicting compressive strength of RBAC is GWO-BP. The predicted/experimental values obtained by the model on the training set and the testing set are 0.98 and 0.96, respectively, and the determination coefficients R2 are 0.97 and 0.99, respectively. SHapley Additive exPlanations (SHAP) is used to analyze the mechanism of design parameters on the compressive strength of RBAC, and the key parameters affecting the compressive strength of RBAC are cement content (Cement) and water-cement ratio (W/C). The Multi-objective optimization (MOO) model is used to target the compressive strength, carbon emission and cost of RBAC, and the scheme with the highest score is determined as the best design scheme of RBAC mix ratio by determining the Pareto optimal solution set of RBAC. The results show that the design method proposed in this paper can effectively guide the mix design of RBAC, the machine learning model optimized by swarm intelligence can effectively predict the compressive strength of RBAC concrete, while the developed MOO model can effectively determine the optimal mix design method.

关键词： Recycled brick aggregate concrete Mix ratio design Machine learning swarm intelligence algorithms Multi -objective optimization

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A Multi-Stage Adaptive Sequential Parameter Exploration Hunger Games Search Algorithm for Solving Complex Optimization Problems

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IEEE ACCESS 2023年 11卷 100919-100947页

作者： Zhan, Bihao Gu, Wei Hubei Univ Technol Sch Comp Sci Wuhan 430074 Peoples R China

Hunger Games Search (HGS) is a newly developed metaheuristic algorithm that models the hunger-driven activities and behaviors of animals. It incorporates the concept of hunger to devise an adaptive weight that mimics the impact of hunger on each search step. In this paper, a Multi-Stage Adaptive Sequential Parameter Exploration Hunger Games Search Algorithm (MASPE-HGSA) is proposed to alleviate the shortcomings of the original HGS in terms of insufficient optimization and convergence accuracy. In MASPE-HGSA, a Multi-Stage adaptive sequential parameter exploration is proposed to improve the search performance of the algorithm as well as to increase the search accuracy and global search capability, which can commendably achieve the balance of exploration and exploitation. The effectiveness of MASPE-HGSA is verified by comparing with original HGS algorithm and several classical algorithms using 23 benchmark functions, CEC2014 test set and three advanced algorithmic problems from classical engineering. Experimental results show that the performance of MASPE-HGSA is significantly better than other similar algorithms. The proposed algorithm can effectively search for high-quality solutions and prevent premature convergence, with better convergence robustness than the original HGS algorithm. In addition, an analysis of probability-based algorithm mechanisms approaching zero is presented in this paper from a theoretical perspective, while providing a reference and verification method for the design of algorithm mechanisms.

关键词： swarm intelligence algorithms hunger games search multi-stage adaptive sequential parameter exploration mechanism engineering design problem

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Virtual reference feedback tuning with robustness constraints: A swarm intelligence solution

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ENGINEERING APPLICATIONS OF ARTIFICIAL intelligence 2023年第PartC期123卷

作者： Fiorio, Luan Vinicius Remes, Chrystian Lenon Wheeler, Patrick de Novaes, Yales Romulo Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands Santa Catarina State Univ Dept Elect Engn BR-89219710 Joinville Brazil Univ Fed Parana Dept Elect Engn BR-80060000 Curitiba Brazil Univ Fed Rio Grande do Sul Dept Elect Engn BR-90035190 Porto Alegre Brazil Univ Nottingham Dept Elect & Elect Engn Nottingham NG7 2RD England

The simplified modeling of a complex system allied with a low-order controller structure can lead to poor closed-loop performance and robustness. A feasible solution is to avoid the necessity of a model by using data for the controller design. The Virtual Reference Feedback Tuning (VRFT) is a data-driven design method that only requires a single batch of data and solves a reference tracking problem, although with no guarantee of robustness. In this work, the inclusion of an H & INFIN;robustness constraint to the VRFT cost function is addressed. The estimation of the H & INFIN;norm of the sensitivity transfer function is extended to maintain the one-shot characteristic of the VRFT. swarm intelligence algorithms are used to solve the non-convex cost function. The proposed method is applied in two real-world inspired problems with four different swarm intelligence algorithms, which are compared with each other through a Monte Carlo experiment of 50 executions. The obtained results are satisfactory, achieving the desired robustness criteria.

关键词： Data-driven control Robust control swarm intelligence algorithms Virtual reference feedback tuning

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Fuzzy optimization of radon reduction by ventilation system in uranium mine

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第6期55卷 2222-2229页

作者： Zhang, Meirong Dai, Jianyong Univ South China Sch Resources Environm & Safety Engn Hengyang 421001 Hunan Peoples R China Key Lab Emergency Safety Technol & Equipment Nucl Hengyang 421001 Hunan Peoples R China

Radon and radon progeny being natural radioactive pollutants, seriously affect the health of uranium miners. Radon reduction by ventilation is an essential means to improve the working environment. Firstly, the relational model is built between the radon exhalation rate of the loose body and the ventilation parameters in the stope with radon percolation-diffusion migration dynamics. Secondly, the model parameters of radon exhalation dynamics are uncertain and described by triangular membership functions. The objective functions of the left and right equations of the radon exhalation model are constructed according to different possibility levels, and their extreme value intervals are obtained by the immune particle swarm optimization algorithm (IPSO). The fuzzy target and fuzzy constraint models of radon exhalation are constructed, respectively. Lastly, the fuzzy aggregation function is reconstructed according to the importance of the fuzzy target and fuzzy constraint models. The optimal control de-cision with different possibility levels and importance can be obtained using the swarm intelligence algorithm. The case study indicates that the fuzzy aggregation function of radon exhalation has an up-ward trend with the increase of the cut set, and fuzzy optimization provides the optimal decision-making database of radon treatment and prevention under different decision-making criteria.(c) 2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Radon reduction by ventilation Radioactive pollutants Radon exhalation dynamics Fuzzy optimization swarm intelligence algorithms

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swarm Robotics for Autonomous Aerial Robots: Features, algorithms, Control Techniques, and Challenges 4

Swarm Robotics for Autonomous Aerial Robots: Features, Algor...

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4th International Conference on Emerging Smart Technologies and Applications (eSmarTA)

作者： Alqudsi, Yunes Makaraci, Murat Kocaeli Univ Fac Aeronaut & Astronaut Dept Aerosp Engn Kocaeli Turkiye Kocaeli Univ Dept Mech Engn Fac Engn Kocaeli Turkiye Taiz Univ Fac Engn IT Mech & Robot Engn Dept Taizi Yemen

ISBN: (纸本)9798350354140;9798350354133

With the aim of revolutionizing various industries, the field of swarm Robotics (SRs) continuously offers efficient solutions to complex tasks. This paper provides a comprehensive review of recent advancements and emerging trends in SR, with a specific focus on the coordination and control of swarm Aerial Robots (SARs). It examines the key features and characteristics of SR, its integration with swarm intelligence, and fundamental algorithms such as Ant Colony Optimization (ACO), Particle swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE). The paper also discusses various swarm control techniques, infrastructure requirements, and challenges associated with implementing SR in Aerial Robots. Additionally, the paper outlines future research directions, emphasizing the need for robust, energy-efficient, adaptive algorithms and ethical considerations in deploying autonomous Aerial robot swarms. This study aims to provide a solid foundation for researchers and practitioners, paving the way for future advancements and interdisciplinary collaboration in this promising field.

关键词： Autonomous Robot swarms swarm intelligence algorithms Robotic Coordination Challenges swarm Control Techniques Multi-Robot Systems

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Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems

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MATHEMATICS AND COMPUTERS IN SIMULATION 2022年 192卷 84-110页

作者： Hashim, Fatma A. Houssein, Essam H. Hussain, Kashif Mabrouk, Mai S. Al-Atabany, Walid Helwan Univ Fac Engn Helwan Egypt Minia Univ Fac Comp & Informat Al Minya Egypt Bahria Univ Dept Comp Sci Karachi Campus Karachi Pakistan Misr Univ Sci & Technol Fac Engn Giza Egypt Nile Univ Informat Technol & Comp Sci Sch Giza Egypt Helwan Univ Biomed Engn Dept Helwan Egypt

Recently, the numerical optimization field has attracted the research community to propose and develop various metaheuristic optimization algorithms. This paper presents a new metaheuristic optimization algorithm called Honey Badger Algorithm (HBA). The proposed algorithm is inspired from the intelligent foraging behavior of honey badger, to mathematically develop an efficient search strategy for solving optimization problems. The dynamic search behavior of honey badger with digging and honey finding approaches are formulated into exploration and exploitation phases in HBA. Moreover, with controlled randomization techniques, HBA maintains ample population diversity even towards the end of the search process. To assess the efficiency of HBA, 24 standard benchmark functions, CEC' 17 test-suite, and four engineering design problems are solved. The solutions obtained using the HBA have been compared with ten well-known metaheuristic algorithms including Simulated annealing (SA), Particle swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Success-History based Adaptive Differential Evolution variants with linear population size reduction (L-SHADE), Moth-flame Optimization (MFO), Elephant Herding Optimization (EHO), Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Thermal Exchange Optimization (TEO) and Harris hawks optimization (HHO). The experimental results, along with statistical analysis, reveal the effectiveness of HBA for solving optimization problems with complex search-space, as well as, its superiority in terms of convergence speed and exploration-exploitation balance, as compared to other methods used in this study. The source code of HBA is currently available for public at https://***/mallabcentral/fileexchange/98204-honey-badger-algorithm(C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Exploration and exploitation Honey Badger Algorithm Meta-heuristic algorithms Nature-inspired algorithms Optimization problems swarm intelligence algorithms

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