检索结果-南通市图书馆

An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and opposition-based learning for Engineering Problems and Its Applications in Breast Cancer Problem

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第11期141卷 1067-1110页

作者： Feyza AltunbeyÖzbay ErdalÖzbay Farhad Soleimanian Gharehchopogh Department of Software Engineering Firat UniversityElazig23119Turkey Department of Computer Engineering Firat UniversityElazig23119Turkey Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia44867-57159Iran

Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in ***,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local *** overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)*** adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space *** maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive *** proposed COARO algorithm has been tested using thirty-three distinct benchmark *** outcomes have been compared with the most recent optimization ***,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other *** study also introduces a binary variant of the continuous COARO algorithm,named *** performance of BCOARO was evaluated on the breast cancer *** effectiveness of BCOARO has been compared with different feature selection *** proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness *** experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.

关键词： Artificial rabbit optimization binary optimization breast cancer chaotic local search engineering design problem opposition-based learning

A Spider Monkey Optimization Algorithm Combining opposition-based learning and Orthogonal Experimental Design

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第9期76卷 3297-3323页

作者： Weizhi Liao Xiaoyun Xia Xiaojun Jia Shigen Shen Helin Zhuang Xianchao Zhang College of Information Science and Engineering Jiaxing UniversityJiaxing314001China School of Information Engineering Huzhou UniversityHuzhou313000China Technology Research and Development Centre Xuelong Group Co.Ltd.Ningbo315899China School of Internet Jiaxing Vocational and Technical CollegeJiaxing314036China

As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent ***,the new space exploration power of SMO is limited and the diversity of the population in SMO is not ***,this paper focuses on how to reconstruct SMO to improve its performance,and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design(SMO^(3))is developed.A position updatingmethod based on the historical optimal domain and particle swarmfor Local Leader Phase(LLP)andGlobal Leader Phase(GLP)is presented to improve the diversity of the population of ***,an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal ***,a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in ***,an extended SMO^(3)named CSMO^(3)is investigated to deal with constrained optimization *** proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering *** results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems.

关键词： Spider monkey optimization opposition-based learning orthogonal experimental design particle swarm

opposition-based learning Harmony Search Algorithm with Mutation for Solving Global Optimization Problems

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Opposition-Based Learning Harmony Search Algorithm with Muta...

Hybrid Modified Chimp Optimization Algorithm and Reinforcement learning for Global Numeric Optimization

第26届中国控制与决策会议

作者： Hao WANG Haibin OUYANG Liqun GAO Wei QIN School of Information Science & Engineering Northeastern University Information engineering department LiaoNing provincial College of Communications China Academy of Aerospace Aerodynamics (CAAA)

This paper develops an opposition-based learning harmony search algorithm with mutation（OLHS-M） for solving global continuous optimization problems. The proposed method is different from the original harmony search（HS） in three aspects. Firstly, opposition-based learning technique is incorporated to the process of improvisation to enlarge the algorithm search space. Then, a new modified mutation strategy is instead of the original pitch adjustment operation of HS to further improve the search ability of HS. Effective self-adaptive strategy is presented to fine-tune the key control parameters（e.g. harmony memory consideration rate HMCR, and pitch adjustment rate PAR） to balance the local and global search in the evolution of the search process. Numerical results demonstrate that the proposed algorithm performs much better than the existing improved HS variants that reported in recent literature in terms of the solution quality and the stability.

关键词： Harmony Search Algorithm Mutation Operation opposition-based learning Search Space Stability

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Journal of Bionic Engineering 2023年第6期20卷 2896-2915页

作者： Mohammad ShDaoud Mohammad Shehab Laith Abualigah Cuong-Le Thanh College of Engineering Al Ain University112612 Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman 11953Jordan Center for Engineering Application Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh City 700000Vietnam

Chimp Optimization Algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various ***,ChOA suffers from the weakness of the local search technique which leads to a loss of diversity,getting stuck in a local minimum,and procuring premature *** response to these defects,this paper proposes an improved ChOA algorithm based on using opposition-based learning(OBL)to enhance the choice of better solutions,written as ***,utilizing Reinforcement learning(RL)to improve the local research technique of OChOA,called *** way effectively avoids the algorithm falling into local *** performance of the proposed RLOChOA algorithm is evaluated using the Friedman rank test on a set of CEC 2015 and CEC 2017 benchmark functions problems and a set of CEC 2011 real-world *** results and statistical experiments show that RLOChOA provides better solution quality,convergence accuracy and stability compared with other state-of-the-art algorithms.

关键词： Chimp optimization algorithm Reinforcement learning Disruption operator opposition-based learning CEC 2011 real-world problems CEC 2015 and CEC 2017 benchmark functions problems

Feature selection of BOF steelmaking process data by using an improved grey wolf optimizer

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Iron and Steel Research International 2022年第8期29卷 1205-1223页

作者： Zong-xin Chen Hui Liu Long Qi Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500YunnanChina Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500YunnanChina

Basic oxygen furnace(BOF)steelmaking end-point control using soft measurement models has essential value for economy and ***,the high-dimensional and redundant data of the BOF collected by the sensors will hinder the performance of *** traditional feature selection results based on meta-heuristic algorithms cannot meet the stability of actual industrial *** order to eliminate the negative impact of feature selection application in the BOF steelmaking,an improved grey wolf optimizer(IGWO)for feature selection was proposed,and it was applied to the BOF data ***,the proposed algorithm preset the size of the feature subset based on the new encoding scheme,rather than the traditional uncertain number ***,opposition-based learning was used to initialize the grey wolf population so that the initial population was closer to the potential optimal *** addition,a novel population update method retained the features closely related to the best three grey wolves and probabilistically updated irrelevant features through measurement or random *** methods were used to search feature subsets to maximize search capability and stability of algorithm on BOF steelmaking ***,the proposed algorithm was compared with other feature selection algorithms on the BOF data *** results show that the proposed IGWO can stably select the feature subsets that are conductive to the end-point regression accuracy control of BOF temperature and carbon content,which can improve the performance of the BOF steelmaking.

关键词： Basic oxygen furnace Feature selection Grey wolf optimization Regression opposition-based learning

Support vector regression-based operational effectiveness evaluation approach to reconnaissance satellite system

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2023年第6期34卷 1626-1644页

作者： HAN Chi XIONG Wei XIONG Minghui LIU Zhen College of Aerospace Information Space Engineering UniversityBeijing 101400China Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering UniversityBeijing 101400China

As one of the most important part of weapon system of systems(WSoS),quantitative evaluation of reconnaissance satellite system(RSS)is indispensable during its construction and *** at the problem of nonlinear effectiveness evaluation under small sample conditions,we propose an evaluation method based on support vector regression(SVR)to effectively address the defects of traditional *** the performance of SVR is influenced by the penalty factor,kernel type,and other parameters deeply,the improved grey wolf optimizer(IGWO)is employed for parameter *** the proposed IGWO algorithm,the opposition-based learning strategy is adopted to increase the probability of avoiding the local optima,the mutation operator is used to escape from premature convergence and differential convergence factors are applied to increase the rate of *** experiments of 14 test functions validate the applicability of IGWO algorithm dealing with global *** index system and evaluation method are constructed based on the characteristics of *** validate the proposed IGWO-SVR evaluation method,eight benchmark data sets and combat simulation are employed to estimate the evaluation accuracy,convergence performance and computational *** to the experimental results,the proposed method outperforms several prediction based evaluation methods,verifies the superiority and effectiveness in RSS operational effectiveness evaluation.

关键词： reconnaissance satellite system(RSS) support vector regression(SVR) gray wolf optimizer opposition-based learning parameter optimization effectiveness evaluation

An Improved Harris Hawks Optimization Algorithm with Multi-strategy for Community Detection in Social Network

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Journal of Bionic Engineering 2023年第3期20卷 1175-1197页

作者： Farhad Soleimanian Gharehchopogh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia969Iran

The purpose of community detection in complex networks is to identify the structural location of nodes. Complex network methods are usually graphical, with graph nodes representing objects and edges representing connections between things. Communities are node clusters with many internal links but minimal intergroup connections. Although community detection has attracted much attention in social media research, most face functional weaknesses because the structure of society is unclear or the characteristics of nodes in society are not the same. Also, many existing algorithms have complex and costly calculations. This paper proposes different Harris Hawk Optimization (HHO) algorithm methods (such as Improved HHO opposition-based learning(OBL) (IHHOOBL), Improved HHO Lévy Flight (IHHOLF), and Improved HHO Chaotic Map (IHHOCM)) were designed to balance exploitation and exploration in this algorithm for community detection in the social network. The proposed methods are evaluated on 12 different datasets based on NMI and modularity criteria. The findings reveal that the IHHOOBL method has better detection accuracy than IHHOLF and IHHOCM. Also, to offer the efficiency of the , state-of-the-art algorithms have been used as comparisons. The improvement percentage of IHHOOBL compared to the state-of-the-art algorithm is about 7.18%.

关键词： Bionic algorithm Complex network Community detection Harris hawk optimization algorithm opposition-based learning Levy flight Chaotic maps

An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第12期77卷 2935-2969页

作者： Xiang Wang Liangsa Wang Han Li Yibin Guo School of Civil Engineering and Architecture Zhengzhou University of AeronauticsZhengzhou450015China School of Civil and Architectural Engineering Jiangsu University of Science and TechnologyZhenjiang212003China

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal *** address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning *** algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale ***,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during *** work and contributions of this paper are primarily reflected in two ***,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is ***,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)***,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters *** evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced *** experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design *** experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls ***,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting *** comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting *** further confirms that OLCHWOA effectively addresses real-world optimization problems.

关键词： Whale optimization algorithm chaos mechanism opposition-based learning long short-term memory realized volatility

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates opposition-based learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning