检索结果-内蒙古大学图书馆

Integration of metaheuristic operators through unstructured evolutive game theory approach: a novel hybrid methodology

引用

EVOLUTIONARY INTELLIGENCE 2025年第1期18卷 1-28页

作者： Escobar-Cuevas, Hector Cuevas, Erik Lopez, Jesus Perez-Cisneros, Marco Univ Guadalajara Dept Elect CUCEI Av Revoluc 1500 Guadalajara Jalisco Mexico

This paper introduces a novel approach to addressing the complexity of current optimization challenges by developing hybrid metaheuristic algorithms. These algorithms combine the strengths of different strategies to enhance solution identification and refinement. In this work, the integration of metaheuristic operators using the Unstructured Evolutive Game Theory to regulate and merge the advantageous features of the Cheetah Optimizer and Particle Swarm Optimization to solve continuous optimization problems is proposed. The Cheetah Optimizer and Particle Swarm Optimization are chosen for their superior exploitation and exploration capabilities, respectively. Our methodology unfolds in two primary phases: combination and modulation. In the combination phase, Cheetah Optimizer and Particle Swarm Optimization search strategies are merged, creating a unified population that generates new candidate solution positions based on the best solution and a modulation factor. During the modulation phase, pairwise competitions based on Unstructured Evolutive Game Theory assess candidate solutions against the objective function, adjusting their modulation factor and position accordingly. We conducted various experiments to evaluate our approach against the original Cheetah Optimizer and Particle Swarm Optimization, as well as seven other well-known metaheuristic algorithms and hybrid schemes, across 30 benchmark functions in dimensions of 30, 50, and 100. The results reveal that our hybrid scheme outperforms the comparative algorithms, demonstrating enhanced performance, effectiveness, and robustness through a detailed convergence and significance analysis.

关键词： Metaheuristic algorithms hybrid algorithms Cheetah optimizer Particle Swarm optimization Evolutionary game theory Unstructured evolutionary game theory

来源：评论

学校读者我要写书评

暂无评论

Convergence and final performances of optimization algorithms for rainfall- runoff model calibration based on the number of function calls

引用

EARTH SCIENCE INFORMATICS 2025年第2期18卷 1-21页

作者： Ersoy, Zeynep Beril Fistikoglu, Okan Okkan, Umut Derin, Berkay Dokuz Eylul Univ Dept Civil Engn Hydraul Div Izmir Turkiye Balikesir Univ Dept Civil Engn Hydraul Div Balikesir Turkiye

This study investigates the final performance and convergence behavior of 14 optimization algorithms, three of which are hybrids that combine derivative-based local search algorithms with several metaheuristics, for calibrating seven conceptual rainfall-runoff models (CRRMs) over two watersheds in Turkey. The study employs three objective functions: Nash-Sutcliffe Efficiency (NS), log-transformed NS (LNS), and Kling-Gupta Efficiency. The TOPSIS multi-criteria decision-making tool was used to rank the algorithms based on their performance across various levels of number of objective function calls (NOFCs). The study findings highlight the differential responses of optimization algorithms to NOFC variations and offer valuable insights into selecting suitable algorithms for CRRM calibration. Accordingly, as the NOFCs increased from 2500 to 10,000, differential evolution (DE) variants demonstrated remarkable adaptability, emerging as the top performers. In contrast, conventional metaheuristics struggled with improvements despite the increase in function calls, primarily due to premature convergence issues. Moreover, particle swarm optimization (PSO) variants endowed with mutation or derivative schemes performed well at lower NOFCs, but as more extensive exploration became necessary, they showed diminishing returns. The study underscores the superiority of DE variants, which include more complex mutation schemes or are derivative-based, in long-run scenarios where both computational efficiency and calibration accuracy are important.

关键词： Conceptual rainfall-runoff models Model calibration hybrid algorithms The number of objective function calls TOPSIS

来源：评论

学校读者我要写书评

暂无评论

An enhanced hybrid HHL algorithm

引用

PHYSICS LETTERS A 2025年 532卷

作者： Morgan, Jack Ghysels, Eric Mohammadbagherpoor, Hamed UNC Chapel Hill Chapel Hill NC 27599 USA IBM Quantum Yorktown Hts NY USA

We present a classical enhancement to improve the accuracy of the hybrid variant (hybrid HHL) of the quantum algorithm for solving linear systems of equations proposed by Harrow, Hassidim, and Lloyd (HHL). We achieve this by using higher precision quantum estimates of the eigenvalues relevant to the linear system, and a new classical step to guide the eigenvalue inversion part of hybrid HHL. We show that eigenvalue estimates with just two extra bits of precision result in tighter error bounds for our Enhanced hybrid HHL compared to HHL. Our enhancement reduces the error of hybrid HHL by an average of 57 percent on an ideal quantum processor for a representative sample of 2x2 systems. On IBM Torino and IonQ Aria-1 hardware, we see that the error of Enhanced hybrid HHL is on average 13 percent and 20 percent (respectively) less than that of HHL for the same set of systems.

关键词： HHL hybrid algorithms NISQ algorithms Linear algebra

来源：评论

学校读者我要写书评

暂无评论

A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems

引用

APPLIED SCIENCES-BASEL 2025年第8期15卷 4506-4506页

作者： Karakutuk, Asli Kaya Ozdemir, Ozer Eskisehir Tech Univ Rectorate 2 Eylul Campus TR-26555 Eskisehir Turkiye Eskisehir Tech Univ Sci Fac Dept Stat TR-26470 Eskisehir Turkiye

Today, numerous methods have been developed to address various problems, each with its own advantages and limitations. To overcome these limitations, hybrid structures that integrate multiple techniques have emerged as effective computational methods, offering superior performance and efficiency compared to single-method solutions. In this paper, we introduce a basic method that combines the strengths of fuzzy logic, wavelet theory, and kernel-based extreme learning machines to efficiently classify facial expressions. We call this method the Fuzzy Wavelet Mexican Hat Kernel Extreme Learning Machine. To evaluate the classification performance of this mathematically defined hybrid method, we apply it to both an original dataset and the JAFFE dataset. The method is enhanced with various feature extraction methods. On the JAFFE dataset, the algorithm achieved an average classification accuracy of 94.55% when supported with local binary patterns and 94.27% with a histogram of oriented gradients. Moreover, these results outperform those of previous studies conducted on the same dataset. On the original dataset, the proposed method was compared with an extreme learning machine and wavelet neural network, and it was found that the method has remarkable efficiency compared to the other two methods.

关键词： hybrid algorithms facial expressions classification wavelet kernel extreme learning machine

来源：评论

学校读者我要写书评

暂无评论

hybrid biogeography-based evolutionary algorithms

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 30卷 213-224页

作者： Ma, Haiping Simon, Dan Fei, Minrui Shu, Xinzhan Chen, Zixiang Shaoxing Univ Dept Phys & Elect Engn Shaoxing Zhejiang Peoples R China Cleveland State Univ Dept Elect & Comp Engn Cleveland OH 44115 USA Shanghai Univ Sch Mech Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China Zhejiang A&F Univ Sch Informat Engn Dept Comp Hangzhou Zhejiang Peoples R China

hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EM. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the continuous optimization benchmarks from the 2013 Congress on Evolutionary Computation (CEC) and on some real-world traveling salesman problems. The new hybrid EAs include two approaches to hybridization: (1) iteration-level hybridization, in which various EAs and BBO are executed in sequence;and (2) algorithm-level hybridization, which runs various EAs independently and then exchanges information between them using ideas from biogeography. Our empirical study shows that the new hybrid EAs significantly outperforms their constituent algorithms with the selected tuning parameters and generation limits, and algorithm-level hybridization is generally better than iteration-level hybridization. Results also show that the best new hybrid algorithm in this paper is competitive with the algorithms from the 2013 CEC competition. In addition, we show that the new hybrid EAs are generally robust to tuning parameters. In summary, the contribution of this paper is the introduction of biogeography-based hybridization strategies to the EA community. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Evolutionary computation hybrid algorithms Biogeography-based optimization Global optimization Traveling salesman problems

来源：评论

学校读者我要写书评

暂无评论

A comparative study of hybrid estimation distribution algorithms in solving the facility layout problem

引用

EGYPTIAN INFORMATICS JOURNAL 2021年第4期22卷 505-513页

作者： Utamima, Amalia Inst Teknol Sepuluh Nopember Fac Intelligent Elect & Informat Technol Surabaya Indonesia

The Estimation Distribution Algorithm (EDA) is an evolutionary algorithm that uses probabilistic models to create candidate solutions. Previous researchers have suggested various hybrid methods to avoid the premature convergence of EDA. This research conducts a comparative study between several variations of hybridization in EDA with regards to the descriptive statistics in the objective values. This study also proposes a new hybrid approach, named Adapted EDA (AEDA), by adapting the structure of EDA by adding a lottery procedure, an elitism strategy, and a neighborhood search. The proposed AEDA, several hybridizations of EDA, and Genetic Algorithm (GA) plus Tabu Search (TS) are applied to the facility layout design in manufacture - Enhanced Facility Layout Problem (EFLP) - to analyze their solutions. The hybrid EDAs that are being compared are EDA plus GA (EDAGA), EDA plus Particle Swarm Optimization (EDAPSO), the combination of EDAPSO plus TS (EDAhybrid), and AEDA. The experimental results show that the AEDA can significantly improves the solution quality in solving all the EFLP instances compared to other algorithms. (C) 2021 THE AUTHOR. Published by Elsevier BV. on behalf of Faculty of Computers and Artificial Intelligence, Cairo University.

关键词： Estimation distribution algorithm Facility layout problem hybrid algorithms Safe work

来源：评论

学校读者我要写书评

暂无评论

hybrid algorithm for the classification of prostate cancer patients of the MCC-Spain study based on support vector machines and genetic algorithms

引用

NEUROCOMPUTING 2021年 452卷 386-394页

作者： Sanchez Lasheras, Juan Enrique Sanchez Lasheras, Fernando Gonzalez Donquiles, Carmen Tardon, Adonina Castano-Vinyals, Gemma Palazuelos, Camilo Salas, Dolores Martin Sanchez, Vicente de Cos Juez, Francisco Javier Hosp Carmen & Severo Ochoa Anesthesiol & Resuscitat Serv Cangas De Narcea Asturias Spain Univ Oviedo Dept Math Oviedo Spain Ctr Invest Biomed Red Epidemiol & Salud Publ CIBE Madrid Spain Univ Oviedo Universitary Inst Oncol Asturias IUOPA Oviedo Spain Univ Leon Biomed Inst IBIOMED Res Grp Gene Environm Hlth Interact Leon Spain Carlos III Inst Hlth Consortium Biomed Res Epidemiol & Publ Hlth CIBER Madrid Spain ISGlobal Ctr Res Environm Epidemiol CREAL Barcelona Spain Univ Pompeu Fabra UPF Barcelona Spain IMIM Hosp del Mar Med Res Inst Barcelona Barcelona Spain Univ Cantabria IDIVAL Santander Spain FISABIO Publ Hlth Valencia Canc & Publ Hlth Area Valencia Spain Valencian Community Gen Directorate Publ Hlth Valencia Spain Univ Oviedo Dept Mines Prospecting & Exploitat Oviedo Spain

In this novel research a hybrid algorithm is presented, one capable of selecting a set of features that makes it possible to identify individuals who are healthy and those who suffer from prostate cancer. In this research the selection of features is carried out by means of evolutionary algorithms. In previous works, algorithms of this nature have proven their ability to find solutions for optimization problems in a wide range of fields. The present research proposes a novel hybrid algorithm based on genetic algorithms and support vector machines have been developed in order to find the best variables subset for classifying individuals. The results obtained show how well the method performs in comparison to other method-ologies. Cases and controls belong to the study MCC-Spain. (c) 2020 Elsevier B.V. All rights reserved.

关键词： hybrid algorithms Support vector machines Genetic algorithms Prostate cancer Single nucleotide polymorphism

来源：评论

学校读者我要写书评

暂无评论

Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques

引用

AUTOMATIKA 2025年第2期66卷 133-153页

作者： Kumar, Arun Nanthaamornphong, Aziz New Horizon Coll Engn Dept Elect & Commun Engn Bengaluru India Prince Songkla Univ Coll Comp Phuket Thailand

The rapid evolution of wireless communication has necessitated advanced waveform analysis for beyond-fifth-generation (B5G) and sixth-generation (6G) radio networks, focusing on efficient spectrum utilization. There is a need for greater spectrum allotment in data-intensive applications, and new technologies require faster data rates and reduced latency. This study explores hybrid spectrum sensing techniques, combining matched filter (MF) energy detection (ED) and an equal-gain combining-based energy detection Neyman-Pearson threshold estimation technique (EGC-ED-PTh) to enhance waveform detection accuracy in complex environments. The proposed method offers an enhanced signal-to-noise ratio (SNR) by optimizing the detection performance, particularly in low-SNR environments, thereby improving the signal reliability. The proposed algorithms are evaluated in comparison with traditional SS methods, including ED, MF, and cyclostationary feature detection (CFD). Additionally, characteristics including bit error rate (BER), power spectral density (PSD), probability of detection (pd), and probability of false alarm (pfa) were researched and evaluated for 500 and 1000 samples. The simulation findings show that the projected algorithms perform better than the traditional algorithms with minimum sidelobes of - 3024 and pfa effects and achieve a throughput gain of 5 and 4.7 dB compared with the conventional algorithms.

关键词： Spectrum sensing beyond 5G equal-gain combining hybrid algorithms pfa Pd BER PSD

来源：评论

学校读者我要写书评

暂无评论

Mathematical modeling and intelligent optimization of submerged arc welding process parameters using hybrid PSO-GA evolutionary algorithms (vol 12, pg 861, 2020)

引用

NEURAL COMPUTING & APPLICATIONS 2020年第15期32卷 11961-11961页

作者： Choudhary, Ankush Kumar, Manoj Gupta, Munish Kumar Unune, Deepak Rajendra Mia, Mozammel LNM Inst Informat Technol Mech Mechatron Engn Jaipur Rajasthan India Shandong Univ Key Lab High Efficiency & Clean Mech Manufacture Jingshi Rd Jinan Peoples R China Ahsanullah Univ Sci & Technol Mech & Prod Engn Dhaka Bangladesh

In the original publication, the fourth author name was incorrectly published.

关键词： hybrid algorithms Intelligent optimization Mathematical modeling PSO-GA Submerged arc welding

来源：评论

学校读者我要写书评

暂无评论

Minimizing the total flow time in a flow shop with blocking by using hybrid harmony search algorithms

引用

EXPERT SYSTEMS WITH APPLICATIONS 2010年第12期37卷 7929-7936页

作者： Wang, Ling Pan, Quan-Ke Tasgetiren, M. Fatih Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol TNList Dept Automat Beijing 100084 Peoples R China Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Yasar Univ Dept Ind Engn Izmir Turkey

In this paper, three hybrid harmony search (HS) algorithms, namely, hybrid harmony search (hHS) algorithm, hybrid globalbest harmony search (hgHS) algorithm and hybrid modified globalbest harmony search (hmgHS) algorithm, are developed for solving the flow shop scheduling with blocking to minimize the total flow time. Firstly, a largest position value (LPV) rule is proposed to convert continuous harmony vectors into job permutations. Secondly, an initialization scheme based on a variant of the NEH heuristic is presented to construct the initial harmony memory with certain quality and diversity. Thirdly, HS is employed to evolve harmony vectors in the harmony memory to perform exploration, whereas a local search algorithm based on the insert neighborhood is embedded to enhance the local exploitation ability. In addition, some new pitch adjustment rules are developed to well inherit good structures from the globalbest harmony vector. Based on a set of well-known benchmark instances, extensive computational experiments are carried out. Computational results show the effectiveness of the hybrid harmony search algorithms, especially the (hmgHS) algorithm, in solving the blocking flow shop scheduling with total flow time criterion. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Blocking flow shop Total flow time Harmony search Pitch adjustment rule hybrid algorithms

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：