检索结果-内蒙古大学图书馆

Multi-level image thresholding using Otsu and chaotic bat algorithm

NEURAL COMPUTING & APPLICATIONS 2018年第12期29卷 1285-1307页

作者： Satapathy, Suresh Chandra Raja, N. Sri Madhava Rajinikanth, V. Ashour, Amira S. Dey, Nilanjan Anil Neerukonda Inst Technol & Sci Dept Comp Sci & Engn Visakhapatnam 531162 Andhra Pradesh India St Josephs Coll Engn Dept Elect & Instrumentat Engn Chennai 600119 Tamil Nadu India Tanta Univ Dept Elect & Elect Commun Engn Fac Engn Tanta Egypt Techno India Coll Technol Dept Informat Technol Kolkata India

Multi-level thresholding is a helpful tool for several image segmentation applications. Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu's thresholding. In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu's between-class variance and a novel chaotic bat algorithm (CBA). Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images. The proposed procedure is applied on a standard test images set of sizes (512 x 512) and (481 x 321). Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm. The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search. The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives. Therefore, it can be applied in complex image processing such as automatic target recognition.

关键词： Multi-level thresholding bat algorithm Otsu method Ikeda Map Peak signal to noise ratio (PSNR) Structural similarity index (SSIM)

来源：评论

学校读者我要写书评

暂无评论

An improved bat algorithm for collaborative dynamic target tracking and path planning of multiple UAV

引用

COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期118卷

作者： Shujuan, Huang Wenqi, Chen Beixuan, Lu Feng, Xiao Chao, Shen Wenjuan, Zhang Xian Technol Univ Sch Comp Sci & Engn 2 XueFu Middle Rd Xian 710072 Shaanxi Peoples R China Xian Technol Univ Sch Sci 2 XueFu Middle Rd Xian 710072 Shaanxi Peoples R China

It is well known that multi-UAV cooperative dynamic target path planning is a challenging field. In this field, multi-UAV cooperative dynamic target path planning is very important to achieve efficient task completion. However, the existing algorithms have some limitations in solving the problems of insufficient search range, premature convergence, local optimization and insufficient population diversity. In order to solve these problems and improve the efficiency and accuracy of path planning, this paper proposes an innovative method. Firstly, we use A* algorithm to obtain the initial assignment result of UAV target, and then use Hungarian algorithm and genetic algorithm to optimize the assignment result and expand the target assignment range. Secondly, in the path planning stage, the bat algorithm is improved, and sine function, dynamic expansion factor and nonlinear function are introduced to solve the problems of insufficient search range, premature convergence and local optimization. At the same time, genetic algorithm is used to solve the problem of insufficient population. By optimizing target assignment and path planning, the performance of multi-UAV cooperative system is improved, so as to better adapt to the task requirements in dynamic environment and provide more reliable solutions for UAV applications. The experimental results show that the tracking and path planning are more effective than BA, CCWOA and OUMPOA under the condition of good stability. Compared with BA, the fitness function and convergence speed are improved by 34.64% and 30.29% respectively.

关键词： Multi-UAV bat algorithm Hungarian algorithm Dynamic function Genetic algorithm Dynamic interference coefficient

来源：评论

学校读者我要写书评

暂无评论

A new modified bat algorithm for global optimization

引用

RAIRO-OPERATIONS RESEARCH 2023年第5期57卷 2659-2685页

作者： Adil, Nouhaila Lakhbab, Halima Hassan II Univ Fac Sci Ain Chock Dept Math & Comp Sci Casablanca Morocco

bat algorithm, is an evolutionary computation technique based on the echolocation behaviour of microbats while looking for their prey. It is used to perform global optimization. It was developed by Xin-She Yang in 2010. Since then, it has extensively been applied in various optimization problems because of its simple structure and robust performance. Continuous, discrete, or binary, many variants were proposed over the last few years, with applications to solve real-world cases in different fields. Yet, it has one major drawback: its premature convergence due to a lack in its exploration ability. In this paper, we introduce a selection-based improvement and three other modifications to the standard version of this metaheuristic in order to enhance the diversification and intensification capabilities of the algorithm. The newly proposed method has been then tested on 20 standard benchmark functions and the CEC2005 benchmark suit. Some non-parametric statistical tests were also used to compare the New bat algorithm with other algorithms, and results indicate that the new method is very competitive and outperforms some of the state-of-the-art algorithms.

关键词： bat algorithm metaheuristics swarm intelligence roulette wheel selection

来源：评论

学校读者我要写书评

暂无评论

A Complex-valued Encoding bat algorithm for Solving 0-1 Knapsack Problem

引用

NEURAL PROCESSING LETTERS 2016年第2期44卷 407-430页

作者： Zhou, Yongquan Li, Liangliang Ma, Mingzhi Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

This paper proposes a novel complex-valued encoding bat algorithm (CPBA) for solving 0-1 knapsack problem. The complex-valued encoding method which can be considered as an efficient global optimization strategy is introduced to the bat algorithm. Based on the two-dimensional properties of the complex number, the real and imaginary parts of complex number are updated separately. The proposed algorithm can effectively diversify bat population and improving the convergence performance. The CPBA enhances exploration ability and is effective for solving both small-scale and large-scale 0-1 knapsack problem. Finally, numerical simulation is carried out, and the comparison results with some existing algorithms demonstrate the validity and stability of the proposed algorithm.

关键词： Complex-valued encoding bat algorithm Knapsack problem

来源：评论

学校读者我要写书评

暂无评论

PI controller Optimization for a Heat Exchanger Through Metaheuristic bat algorithm, Particle Swarm Optimization, Flower Pollination algorithm and Cuckoo Search algorithm

引用

IEEE LATIN AMERICA TRANSACTIONS 2017年第9期15卷 1801-1807页

作者： Damasceno, N. C. Filho, O. G. Univ Potiguar Engn Automacao Proc Gas Nat Natal RN Brazil Univ Fed Rio Grande do Norte Natal RN Brazil

This paper presents the modeling and computer simulation of a control system for a shell and tube heat exchanger, using bat algorithms, Particle Swarm Optimization, Flower Pollination algorithm and Cuckoo Search algorithm. To evaluate the performance of different methods of tuning, we compared the values of the transient of the response to step in eight mesh settings generated. It has also established a comparison between these two types of mesh using the performance indices proposed in the literature, with optimized system by bat algorithms got the best values of transient in relation to the Particle Swarm Optimization, Cuckoo Search algorithm and Flower Pollination algorithm. Performance indices FPA and PSO obtained better results.

关键词： PI controller bat algorithm PSO Flower Pollination algorithm Heat Exchanger Optimization

来源：评论

学校读者我要写书评

暂无评论

Hybrid Cloud Resource Provisioning (HCRP) algorithm for Optimal Resource Allocation Using MKFCM and bat algorithm

引用

WIRELESS PERSONAL COMMUNICATIONS 2020年第2期111卷 1171-1185页

作者： Kalaiselvi, S. Selvi, C. S. Kanimozhi Kongu Engn Coll Dept Comp Technol Erode Tamil Nadu India Kongu Engn Coll Dept Comp Sci & Engn Erode Tamil Nadu India

The basic purpose of resource allocation is to make the most efficient allocation of available resources. It contains resources and the number of tasks. The proposed methodology has two types there are resource discovery and resource allocation. The Multiple Kernel Fuzzy C Means Clustering algorithm (MKFCM) is utilized for resource discovery process. Depends on the MKFCM algorithm the recommended method is group the available resources. Thereafter the resources are allocated with the help of a hybrid optimization technique. Here, resource provisioning algorithm is hybrid with bat algorithm for hybridization approach. The experimental analysis of the proposed mechanism is evaluated based on cost value, memory utilization and time. The prospective strategies have been experimented using the Cloud simulator with Java as the working platform.

关键词： Resource discovery Resource allocation Multiple Kernel Fuzzy C Means Clustering Cloud resource provisioning bat algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel bat algorithm with dynamic membrane structure for optimization problems

引用

APPLIED INTELLIGENCE 2021年第4期51卷 1992-2017页

作者： Alsalibi, Bisan Abualigah, Laith Khader, Ahamad Tajudin Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan

To improve the optimization efficiency for different optimization problems and take advantage of the dynamic membrane computing framework, this paper proposes an improved bat algorithm, namely, Dynamic Membrane-driven bat algorithm (DMBA). The dynamic construction of the DMBA algorithm aims at enhancing population diversity by balancing the exploration-exploitation tradeoff. Unlike the static membrane algorithms, the membranes in DMBA will be dynamically evolved by using merging and separation rules which help in maintaining the diversity of the population. The experimental results on a set of well-known benchmark functions including CEC 2005, CEC 2011, and CEC 2017 clearly prove the effectiveness of the proposed DMBA algorithm in terms of maintaining the diversity and exploitation capabilities compared to that of the others. It is shown that the proposed DMBA algorithm is superior to recent variants of the bat algorithm and other well-known algorithms in terms of solution accuracy and convergence speed.

关键词： Dynamic membrane structure Parallel membrane framework bat algorithm Optimization problems

来源：评论

学校读者我要写书评

暂无评论

A novel complex-valued bat algorithm

引用

NEURAL COMPUTING & APPLICATIONS 2014年第6期25卷 1369-1381页

作者： Li, Liangliang Zhou, Yongquan Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Guangxi Peoples R China Guangxi Key Lab Hybrid Computat & Integrated Circ Nanning 530006 Guangxi Peoples R China

bat algorithm is a recent optimization algorithm with quick convergence, but its population diversity can be limited in some applications. This paper presents a new bat algorithm based on complex-valued encoding where the real part and the imaginary part will be updated separately. This approach can increase the diversity of the population and expands the dimensions for denoting. The simulation results of fourteen benchmark test functions show that the proposed algorithm is effective and feasible. Compared to the real-valued bat algorithm or particle swarm optimization, the proposed algorithm can get high precision and can almost reach the theoretical value.

关键词： Complex-valued encoding bat algorithm Diploid Test functions

来源：评论

学校读者我要写书评

暂无评论

Online control of enumeration strategies via bat algorithm and black hole optimization

引用

NATURAL COMPUTING 2017年第2期16卷 241-257页

作者： Soto, Ricardo Crawford, Broderick Olivares, Rodrigo Niklander, Stefanie Johnson, Franklin Paredes, Fernando Olguin, Eduardo Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Valparaiso Valparaiso Chile Univ Autonoma Chile Santiago Chile Univ Espiritu Santo Guayaquil Ecuador Univ Playa Ancha Valparaiso Chile Univ Diego Portales Escuela Ingn Ind Santiago Chile Univ San Sebastian Santiago Chile

Constraint programming is an efficient and powerful paradigm for solving constraint satisfaction and optimization problems. Under this paradigm, problems are modeled as a sequence of variables and a set of constraints. The variables have a non-empty domain of candidate values and constraints restrict the values that variables can adopt. The solving process operates by assigning values to variables in order to produce potential solutions which are then evaluated. A main component in this process is the enumeration strategy, which decides the order in which variables and values are chosen to produce such potential solutions. There exist different ways to perform this selection, and depending on the quality of this decision, the efficiency of the solving process may dramatically vary. Unfortunately, selecting the proper strategy is known to be a hard task, as its behavior during search is generally unpredictable and certainly depends on the problem at hand. A recent trend to handle this concern, is to interleave a set of different strategies instead of using a single one during the whole process. The strategies are evaluated according to process indicators in order to use the most promising one on each part of the search process. This process is known as online control of enumeration strategies and its correct configuration can be seen itself as an optimization problem. In this paper, we present two new systems for online control of enumeration strategies based on recent nature-inspired metaheuristics: bat algorithm and black hole optimization. The bat algorithm mimics the location capabilities of bats that employ echoes to identify the objects in their surrounding areas, while black hole optimization inspires its behavior on the gravitational pull of black holes in space. We perform different experimental results by using different enumeration strategies and well-known benchmarks, where the proposed approaches are able to noticeably outperform previous work on online

关键词： Constraint programming Autonomous search Bio-inspired Metaheuristics bat algorithm Black hole

来源：评论

学校读者我要写书评

暂无评论

Modified bat algorithm for Localization of Wireless Sensor Network

引用

WIRELESS PERSONAL COMMUNICATIONS 2016年第2期86卷 657-670页

作者： Goyal, Sonia Patterh, Manjeet Singh Punjabi Univ Dept Elect & Commun Engn Patiala 144011 Punjab India

The problem of node localization in wireless sensor networks aims to assign th e geographical coordinates to each device with unknown position, in the deployment area. In this paper the meta heuristic optimization algorithm known as bat algorithm is described in order to evaluate the precision of node localization problem in wireless sensor networks. Meanwhile the existing bat algorithm has also been modified by using the bacterial foraging strategies of bacterial foraging optimization algorithm. Compared with the existing bat algorithm, the proposed modified bat algorithm is shown through simulations to perform constantly better not only in increasing localization success ratios and fast convergence speed but also enhance its robustness.

关键词： Wireless sensor network Localization bat algorithm Modified bat algorithm

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：