检索结果-内蒙古大学图书馆

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Improved teaching-learning based optimization algorithm using Lyapunov stability analysis

引用

JOURNAL OF AMBIENT intelligence AND HUMANIZED COMPUTING 2020年第7期13卷 3609-3618页

作者： Farivar, Faezeh Shoorehdeli, Mahdi Aliyari Manthouri, Mohammad Islamic Azad Univ Dept Mechatron & Comp Engn Sci & Res Branch Tehran Iran KN Toosi Univ Technol Dept Mechatron Fac Elect Engn Tehran Iran Shahed Univ Elect & Elect Engn Dept Tehran Iran

Teaching-learning-based optimization (TLBO) algorithms is one of the swarm-based optimization search algorithms. It develops based on the teaching-learning procedures at a classroom to solve multi-dimensional and nonlinear problems. In this paper, convergence and stability analysis of TLBO are studied. The stability of individual dynamics is analyzed by Lyapunov stability theorem and the concept of system dynamics. Stability conditions are achieved and utilized for adapting parameters of the TLBO. The TLBO algorithm is modified based on stability analysis. The modified TLBO is compared with the standard TLBO, particle swarm optimization (PSO), real genetic algorithm (RGA), and gravitational search algorithm (GSA). Simulation results confirm the validity and feasibility of the proposed modified TLBO. The appropriate performance is achieved for multi-dimensional and nonlinear standard bench functions.

关键词： Convergence Lyapunov stability Teaching-learning-based optimization swarm intelligence algorithms

来源：评论

学校读者我要写书评

暂无评论

Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems

引用

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

来源：评论

学校读者我要写书评

暂无评论

Multilevel thresholding of images with improved Otsu thresholding by black widow optimization algorithm

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2021年第18期80卷 28217-28243页

作者： Al-Rahlawee, Anfal Thaer Hussein Rahebi, Javad Altinbas Univ Dept Elect & Comp Engn Istanbul Turkey

One of the most important methods of image processing is image thresholding, which is based on image histogram analysis. These methods analyze the image histogram diagram and try to present optimal values for the image thresholds so that the image regions can be distinguished by these thresholds. Thresholding is a popular method in image processing and is used in most research related to image segmentation due to its accuracy and efficiency. Multi-level thresholding, such as the Otsu method, is one of the most common methods of thresholding image processing. These methods have high computational complexity despite their accuracy and efficiency. When the number of thresholds used increases, these methods lose their efficiency due to increased complexity and execution time. One of the ways to find thresholds in the Otsu threshold method is to use metaheuristic algorithms such as the Black Widow Spider Optimization Algorithm. These algorithms can find the appropriate thresholds for the image at the logical time. In the proposed method, each threshold is a component or one dimension of a solution of the Black Widow Spider Optimization Algorithm, and an attempt is made to calculate the optimal threshold value without high complexity by this algorithm. Experiments on several standard images show that the proposed algorithm finds better thresholds than the particle swarm optimization algorithm, the firefly algorithm, the genetic algorithm, and the gray wolf optimization algorithm. The analysis shows that the proposed method in the PSNR index has a better value in 83.33% of the experiments than other algorithms and also in 80% of the experiments the proposed method has a better SSIM index than these methods. Analysis of the proposed algorithm on several pertussis images also shows that the proposed method has a good ability to threshold medical images such as brain tumors and optic disc detection in human retinal images.

关键词： Thresholding Otsu swarm intelligence algorithms Black widow optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal Placement of Near Ground VHF/UHF Radio Communication Network as a Multi Objective Problem

引用

WIRELESS PERSONAL COMMUNICATIONS 2020年第3期110卷 1169-1197页

作者： Shehadeh, Hisham A. Idris, Mohd. Yamani Idna Ahmedy, Ismail Hassen, Hani Ragab Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur Malaysia Heriot Watt Univ Sch Math & Comp Sci Edinburgh Midlothian Scotland

Ultra high frequency (UHF) and very high frequency (VHF) bands are widely used with near ground applications especially for communication purposes. However, due to the limited antenna height, the VHF/UHF signals are very sensitive to the surroundings. The signal may attenuate, fade or loss depending on the condition of the environment. Therefore, it is a challenging task to find the optimal values for network topology planning. In this paper, three multi-objective algorithms namely NSGA-II, SPEA-II and OMOPSO are considered to mitigate the problem. This paper also intends to maximize the electromagnetic fields and signal propagation. On the other hand, it intends to minimize path loss and signal attenuation. Different environment settings such as diverse foliage depth and tree trunk height are considered to evaluate the proposed model. From the experiments, it is shown that NSGA-II outperforms other algorithms. The experiment also shows that the optimal foliage depth between sender and receiver is less or equal to 2 km and the optimal tree trunk height is less or equal to 7 m.

关键词： Near ground network Network planning Network modeling Evolutionary algorithms swarm intelligence algorithms Optimality

来源：评论

学校读者我要写书评

暂无评论

algorithms for path optimizations: a short survey

引用

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

来源：评论

学校读者我要写书评

暂无评论

Cost-Minimized Computation Offloading and User Association in Hybrid Cloud and Edge Computing

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第9期11卷 16672-16683页

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

Smart mobile devices (SMDs) are integral for running advanced applications that demand significant computing resources and quick response time, e.g., immersive gaming and advanced image editing. However, SMDs often face constraints in computational capacity and battery duration, restricting their ability to process these tasks instantaneously. Cloud computing can circumvent these limitations by computation offloading, but cloud data centers (CDCs) are often deployed at long distances from users, which results in longer computational latency. To address the latency issue, the incorporation of small base stations (SBSs) in the vicinity of the user provides services with high bandwidth and low latency. The primary challenge lies in balancing the economics of the system consisting of different SMDs, SBSs, and a CDC, i.e., minimizing cost while still meeting the latency requirements of applications. In this work, a cost-minimized computation offloading framework is formulated and solved by a two-stage optimization algorithm named L & eacute;vy flight and simulated annealing-based grey wolf optimizer (LSAG). The optimal edge selection strategy is defined in the first stage for dealing with the case of several available SBSs. The second stage coordinates task scheduling and optimizes the allocation of resources among SMDs, SBSs, and CDC. LSAG integrates the extended search property of L & eacute;vy flight and the individual selection strategy of simulated annealing in the grey wolf optimizer, which reduces the risk of falling into local optima and finds the global optimum. Experimental results of executing real-life tasks show that LSAG outperforms its state-of-the-art peers in terms of cost and speed of convergence.

关键词： Costs Task analysis Resource management Optimization Energy consumption Servers Computer architecture Cloud computing computation offloading edge computing grey wolf optimizer (GWO) swarm intelligence algorithms

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization

引用

JOURNAL OF SUPERCOMPUTING 2020年第12期76卷 9330-9354页

作者： Jarrah, Muath Ibrahim Jaya, A. S. M. Alqattan, Zakaria N. Azam, Mohd Asyadi Abdullah, Rosni Jarrah, Hazim Abu-Khadrah, Ahmed Ismail Univ Tekn Malaysia Melaka Fac Informat & Commun Technol Durian Tunggal 76100 Melaka Malaysia Univ Sains Malaysia Sch Comp Sci Natl Adv IPv6 Ctr Gelugor 11800 Penang Malaysia Univ Tekn Malaysia Melaka Fac Mfg Engn Durian Tunggal 76100 Melaka Malaysia Auckland Univ Technol Dept Elect & Elect Engn Auckland New Zealand Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia

Over the past few decades, there has been a surge of interest of using swarm intelligence (SI) in computer-aided optimization. SI algorithms have demonstrated their efficacy in solving various types of real-world optimization problems. However, it is impossible to find an optimization algorithm that can obtain the global optimum for every optimization problem. Therefore, researchers extensively try to improve methods of solving complex optimization problems. Many SI search algorithms are widely applied to solve such problems. ABC is one of the most popular algorithms in solving different kinds of optimization problems. However, it has a weak local search performance where the equation of solution search in ABC performs good exploration, but poor exploitation. Besides, it has a fast convergence and can therefore be trapped in the local optima for some complex multimodal problems. In order to address such issues, this paper proposes a novel hybrid ABC with outstanding local search algorithm called beta-hill climbing (beta HC) and denoted by ABC-beta HC. The aim is to improve the exploitation mechanism of the standard ABC. The proposed algorithm was experimentally tested with parameters tuning process and validated using selected benchmark functions with different characteristics, and it was also evaluated and compared with well-known state-of-the-art algorithms. The evaluation process was investigated using different common measurement metrics. The result showed that the proposed ABC-beta HC had faster convergence in most benchmark functions and outperformed eight algorithms including the original ABC in terms of all the selected measurement metrics. For more validation, Wilcoxon's rank sum statistical test was conducted, and the p values were found to be mostly less than 0.05, which demonstrates that the superiority of the proposed ABC-beta HC is statistically significant.

关键词： Metaheuristic optimization algorithms swarm intelligence algorithms Artificial bee colony algorithm beta-Hill climbing Numerical benchmark function optimization

来源：评论

学校读者我要写书评

暂无评论

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

引用

APPLIED intelligence 2017年第3期47卷 793-808页

作者： Khennak, Ilyes Drias, Habiba USTHB Lab Res Artificial Intelligence Comp Sci Dept BP 32 El Alia 16111 Algiers Algeria

swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web's largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

关键词： Information retrieval Web intelligence Query expansion swarm intelligence algorithms Accelerated particle swarm optimization MEDLINE

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：