检索结果-内蒙古大学图书馆

A reinforcement learning based artificial bee colony algorithm with application in robot path planning

EXPERT SYSTEMS WITH APPLICATIONS 2022年 203卷

作者： Cui, Yibing Hu, Wei Rahmani, Ahmed Cent Lille CNRS UMR 9189 CRIStAL F-59651 Villeneuve Dascq France Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

artificial bee colony (ABC) algorithm is a popular optimization algorithm with excellent exploration ability and various applications. Nevertheless, its effectiveness is limited by the one-dimensional search strategy. Therefore, in order to improve its performance, a reinforcement learning (RL) based ABC algorithm is proposed (named ABC_RL). In ABC_RL, the number of dimensions to be updated in search equation of the employed bee phase is varied and adjusted intelligently via RL. Moreover, two improved search strategies are adopted to maintain a nice balance between diversification and intensification. The performance of ABC_RL is evaluated through a series of comparisons conducted on CEC 2017 benchmark problems. The results indicate that ABC_RL outperforms the compared ABC variants considering the solution accuracy. In addition, a robot path planning problem is concerned to further test the effectiveness of ABC_RL. And the comparison results show the advantages of ABC_RL in terms of path length and running time.

关键词： artificial bee colony algorithm Reinforcement learning Mittag-Leffler distribution Differential search equation Robot path planning

来源：评论

学校读者我要写书评

暂无评论

A new artificial bee colony algorithm using a gradual weight method for the bi-objective traveling salesman problems

引用

EVOLUTIONARY INTELLIGENCE 2022年第3期15卷 2077-2088页

作者： Bouzid, Mouna Alaya, Ines Tagina, Moncef Univ Manouba Cosmos ENSI Manouba 2010 Tunisia

The traveling salesman problem (TSP) is a typical problem in combinatorial optimization. The unique objective of the traditional TSP is to minimize the tour distance. When more than one objective is taken into account to be optimized simultaneously, the problem is known as a multi-objective TSP. In the present paper, a bi-objective traveling salesman problem (BOTSP) is considered, where both the cost and the distance are taken as optimization objectives. We introduce a new algorithm that solves the BOTSP called Gw-ABC. This algorithm is based on an artificial bee colony algorithm (ABC) and uses a Gradual Weight Generation method. Firstly, we apply a gradual method to prepare the weight vectors. In fact, weight vectors values are gradually distributed in the objective space and change relatively to the optimization process from cycle to another. Secondly, we apply the ABC algorithm with the generated weights to our problem. The proposed algorithm is experimentally tested on well-known benchmark instances of different sizes and compared with other state-of-the-art algorithms. The obtained experimental results show that Gw-ABC is significantly better.

关键词： artificial bee colony algorithm Gradual weight generated vectors Bi-objective traveling salesman problem

来源：评论

学校读者我要写书评

暂无评论

Multi-level threshold selection based on artificial bee colony algorithm and maximum entropy for image segmentation

引用

INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第4期43卷 343-350页

作者： Xiao, Yonghao Cao, Yunfei Yu, Weiyu Tian, Jing South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China Foshan Univ Foshan 528000 Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Jiangsu Peoples R China

Image threshold segmentation based on artificial bee colony algorithm (ABCA) and maximum entropy is presented in this paper. The entropy function is simplified with several parameters. The ABC is applied to search the maximum value of entropy function. According to the maximum function value, the optimal image thresholds are obtained. Experimental results are provided to demonstrate the superior performance of the proposed approach.

关键词： image threshold maximum entropy artificial bee colony algorithm ABCA image segmentations

来源：评论

学校读者我要写书评

暂无评论

Dynamic Topology Optimization for Multiple Eigenfrequencies using the artificial bee colony algorithm

引用

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2015年第8期16卷 1817-1824页

作者： Chang, Dae-Ho Han, Seog-Young Hanyang Univ Grad Sch Dept Mech Engn Seoul 133791 South Korea Hanyang Univ Div Mech Engn Seoul 133791 South Korea

The purpose of this study is to suggest a method of applying the artificial bee colony algorithm (ABCA) in the frequency topology optimization for a structure with multiple eigenfrequencies. In order to replicate the multiple eigenfrequencies of a structure, sub-optimization procedure for multiple eigenfrequencies was additionally developed. In order to obtain a stable and robust optimal topology the waggle index update rule, evaluation method of fitness values and changing filtering size scheme were also employed And the optimized topologies of ABCA for examples were compared with those of the solid isotropic material with penalization (SIMP) method for investigating the applicability and effectiveness of the ABCA. The following conclusions were obtained through the results of examples;(I) The ABCA implemented with sub-optimization procedure and the three suggested schemes, is very applicable and effective in dynamic topology optimization. (2) The multiple eigenfrequencies of a structure are successfully replicated by the ABCA in optimization procedure. (3) The fundamental frequency of the ABCA is almost the same or slightly higher than that of the SIMP.

关键词： artificial bee colony algorithm Finite element method Multiple eigenfrequencies Stochastic search method Topology optimization

来源：评论

学校读者我要写书评

暂无评论

Improved multi-strategy artificial bee colony algorithm

引用

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2016年第5期7卷 467-475页

作者： Lv, Li Wu, Lieyang Zhao, Jia Wang, Hui Wu, Runxiu Fan, Tanghuai Hu, Min Xie, Zhifeng Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang 330099 Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China Jiangxi Expressway Networking Management Ctr Nanchang 330036 Peoples R China

artificial bee colony (ABC) algorithm is a nature-inspired metaheuristic based on imitating the foraging behaviour of bee, which is widely used in solving complex multi-dimensional optimisation problems. In order to overcome the drawbacks of standard ABC, such as slow convergence and low solution accuracy, we propose an improved multi-strategy artificial bee colony algorithm (MSABC). According to the type of position information in ABC, three basic search mechanisms are summarised, the mechanisms include searching around the individual, the random neighbour and the global best solution. Then, the basic search mechanisms are improved to obtain three search strategies. Each bee randomly selects a search strategy to produce a candidate solution under the same probability in each iteration. Thus these strategies can make a good balance between exploration and exploitation. Finally, the experiments are conducted on eight classical functions. Results show that our algorithm performs significantly better than several recently proposed similar algorithms in terms of the convergence speed and solution accuracy.

关键词： artificial bee colony algorithm random selection strategy information interchange

来源：评论

学校读者我要写书评

暂无评论

artificial bee colony algorithm Based on Information Learning

引用

IEEE TRANSACTIONS ON CYBERNETICS 2015年第12期45卷 2827-2839页

作者： Gao, Wei-Feng Huang, Ling-Ling Liu, San-Yang Dai, Cai China Univ Petr Qingdao 266580 Peoples R China Xidian Univ Sch Sci Xian 710071 Peoples R China Shaanxi Normal Univ Coll Comp Sci Xian 710062 Peoples R China

Inspired by the fact that the division of labor and cooperation play extremely important roles in the human history development, this paper develops a novel artificial bee colony algorithm based on information learning (ILABC, for short). In ILABC, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of subpopulation is dynamically adjusted based on the last search experience, which results in a clear division of labor. Furthermore, the two search mechanisms are designed to facilitate the exchange of information in each subpopulation and between different subpopulations, respectively, which acts as the cooperation. Finally, the comparison results on a number of benchmark functions demonstrate that the proposed method performs competitively and effectively when compared to the selected state-of-the-art algorithms.

关键词： artificial bee colony algorithm clustering partition search equation search mechanism

来源：评论

学校读者我要写书评

暂无评论

A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem

引用

APPLIED SOFT COMPUTING 2013年第3期13卷 1459-1463页

作者： Liu, Yan-Feng Liu, San-Yang Xidian Univ Dept Math Xian 710071 Peoples R China

To minimize the makespan in permutation flowshop scheduling problems, a hybrid discrete artificial bee colony (HDABC) algorithm is presented. In the HDABC, each solution to the problem is called a food source and represented by a discrete job permutation. First, the initial population with certain quality and diversity is generated from Greedy Randomized Adaptive Search Procedure (GRASP) based on Nawaz-Enscore-Ham (NEH) heuristics. Second, the discrete operators and algorithm, such as insert, swap, path relinking and GRASP are applied to generate new solution for the employed bees, onlookers and scouts. Moreover, local search is applied to the best one. The presented algorithm is tested on scheduling problem benchmarks. Experimental results show its efficiency. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Scheduling Permutation flowshop scheduling artificial bee colony algorithm GRASP NEH heuristics

来源：评论

学校读者我要写书评

暂无评论

Adaptive filtering noisy transcranial Doppler signal by using artificial bee colony algorithm

引用

ENGINEERING APPLICATIONS OF artificial INTELLIGENCE 2013年第2期26卷 677-684页

作者： Karaboga, Nurhan Latifoglu, Fatma Erciyes Univ Fac Engn Elect & Elect Eng Dept Kayseri Turkey Erciyes Univ Biomed Eng Dept Fac Engn Kayseri Turkey

Computerized processes are supportive in the new age of medical treatment. Biomedical signals which are collected from the human body supply or important useful data that are related with the biological actions of human body organs. However, these signals may also contain some noise. Heart waves are commonly classified as biomedical signals and are non-stationary due to their statistical specifications. The probability distributions of the noise are very different, and for this reason there is no common method to remove the noise. In this study, adaptive filters are used for noise elimination and the transcranial Doppler signal is analyzed. The artificial bee colony algorithm was employed to design the adaptive IIR filters for noise elimination on the transcranial Doppler signal and the results were compared to those obtained by the methods based on popular and recently introduced evolutionary algorithms and conventional methods. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm PSO algorithm DE algorithm Adaptive IIR filter Noise elimination Transcranial Doppler signal

来源：评论

学校读者我要写书评

暂无评论

Optimal design of damping controllers using a new hybrid artificial bee colony algorithm

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期52卷 42-54页

作者： Eslami, Mandiyeh Shareef, Hussain Khajehzadeh, Mohammad Natl Univ Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Islamic Azad Univ Anar Branch Dept Civil Engn Anar Iran

This paper integrates the artificial bee colony (ABC) algorithm with the sequential quadratic programming (SQP) to create the new hybrid optimization algorithm, ABC-SQP, for solving global optimization problems and damping of low frequency oscillations in power system stability analyses. The new algorithm combines the global exploration ability of ABC to converge rapidly to a near optimum solution and the accurate local exploitation ability of SQP to accelerate the search process and find an accurate solution. A set of well-known benchmark optimization problems is used to validate the performance of the ABC-SQP as a global optimization algorithm and to facilitate a comparison with the classical ABC. Numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions. Power system stabilizers and supplementary static VAR compensator controllers are designed for two-area-four-machine and five-area-sixteen-machine systems to illustrate the feasibility and effectiveness of the new method in power systems. The performance of the proposed ABC-SQP algorithm is compared with the classic ABC and the genetic algorithm (GA) through eigenvalue analysis and nonlinear time-domain simulation. The simulation results indicate that the controllers designed by the ABC-SQP perform better than those designed by ABC and GA. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Power system stabilizer Static VAR compensator Low-frequency oscillation Hybrid algorithm artificial bee colony algorithm Sequential quadratic programming

来源：评论

学校读者我要写书评

暂无评论

Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

引用

WIRELESS NETWORKS 2022年第4期28卷 1671-1688页

作者： Lu Sixu Wu Muqing Zhao Min Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Networks Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China

With the development of the internet of things, people pay more and more attention to wireless sensor networks. Designing the energy efficient routing is an essential objective for wireless sensor networks. Cluster routing is one of the most popular routing protocols to enhance the network lifetime. However, hotspot problem always exists in cluster-based routing protocol. The task of this study is designing a cluster routing protocol with mobile base station which aims at balancing the energy consumption and prolonging the network lifetime. In this article, we design a particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks. The software defined network architecture is used to reduce the energy overhead and computation overhead in sensor nodes. Particle swarm optimization-based cluster routing algorithm is used to calculate the cluster heads and the sojourn locations of base station. artificial bee colony algorithm-based traversal path algorithm is used to design the move path of the base station. Comparing with relevant protocols, the proposed protocol reduces the energy consumption, enhances the network lifetime and reduces the control overhead.

关键词： Software-defined wireless sensor networks Cluster routing Mobile base station Particle swarm optimization artificial bee colony 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：