检索结果-内蒙古大学图书馆

A hybrid meta-heuristic algorithm for flowshop robust scheduling under machine breakdown uncertainty

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2016年第7期29卷 709-719页

作者： Fazayeli, Mohammad Aleagha, Mohammad-Reza Bashirzadeh, Reza Shafaei, Rasoul KNT Univ Technol Dept Ind Engn Tehran Iran

One of the most important assumptions in production scheduling is permanent availability of the machines without any breakdown. In real-world scheduling problems, machines could be unavailable due to various reasons such as preventive maintenance and unpredicted breakdowns. In this paper, a flowshop scheduling problem under machine breakdown uncertainty is studied. The machines are subject to breakdown in practice caused by components' wear-out. A proactive scheduling is considered to deal with unpredictable machine breakdown. An effective hybrid meta-heuristic algorithm based on genetic and simulated annealing algorithms is proposed to tackle such an NP-hard problem. To evaluate the performance of the proposed algorithm, its performance in terms of maximising the beta-robustness of makespan was compared with six other heuristic and meta-heuristic algorithms. Computational results confirm that the proposed algorithm outperforms the others.

关键词： flowshop robust schedule meta-heuristic algorithm uncertainty machine breakdown beta-robustness

来源：评论

学校读者我要写书评

暂无评论

Optimal solution to the vehicle routing problem by adopting a meta-heuristic algorithm

引用

TRANSPORTATION PLANNING AND TECHNOLOGY 2016年第6期39卷 574-585页

作者： Kim, Seung Hyun Bae, Sang Hoon Pukyong Natl Univ Dept Spatial Informat Engn Busan South Korea

The delivery service provided by large-scale retailers continues to grow as online sales occupy an increasingly large share of the market. This study aims to tease out efficient vehicle scheduling times as well as optimal delivery routes by applying meta-heuristic algorithms. Monthly data on existing routes were obtained from a branch of Korea's leading large-scale online retailer. The first task was to examine the status of existing routes by comparing delivery routes created using Dijkstra's algorithm with existing delivery routes and their vehicle scheduling. The second task was to identify optimal delivery routes through a comparative analysis of the genetic algorithm and Tabu search algorithm, known for its superior applicability amongst other meta-heuristic algorithms. These findings demonstrate that the optimal vehicle routing problem not only has the potential to reduce distribution costs for operators and expedite delivery for consumers, but also the added social benefit of reduced carbon emissions.

关键词： Dynamic vehicle routing problem meta-heuristic algorithm tabu search linear optimization cost reduction

来源：评论

学校读者我要写书评

暂无评论

A new meta-heuristic optimization algorithm using star graph

引用

SMART STRUCTURES AND SYSTEMS 2017年第1期20卷 99-114页

作者： Gharebaghi, Saeed Asil Kaveh, Ali Asl, Mohammad Ardalan KN Toosi Univ Technol Dept Civil Engn Tehran Iran Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn Tehran 16 Iran

In cognitive science, it is illustrated how the collective opinions of a group of individuals answers to questions involving quantity estimation. One example of this approach is introduced in this article as Star Graph (SG) algorithm. This graph describes the details of communication among individuals to share their information and make a new decision. A new labyrinthine network of neighbors is defined in the decision-making process of the algorithm. In order to prevent getting trapped in local optima, the neighboring networks are regenerated in each iteration of the algorithm. In this algorithm, the normal distribution is utilized for a group of agents with the best results (guidance group) to replace the existing infeasible solutions. Here, some new functions are introduced to provide a high convergence for the method. These functions not only increase the local and global search capabilities but also require less computational effort. Various benchmark functions and engineering problems are examined and the results are compared with those of some other algorithms to show the capability and performance of the presented method.

关键词： meta-heuristic algorithm global optimization graph theory optimal design truss structures frame structures

来源：评论

学校读者我要写书评

暂无评论

Competitive Learning: A New meta-heuristic Optimization algorithm

引用

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2018年第8期27卷

作者： Afroughinia, Afshin Moghaddam, Reihaneh Kardehi Islamic Azad Univ Dept Elect Engn Mashhad Branch Mashhad Iran

This work proposes a new powerful meta-heuristic optimization algorithm in education process called Competitive Learning (CLA). The algorithm is benchmarked on 8 well-known test functions, and the results are verified by a comparative study with some meta-heuristic optimization methods including: Imperialist Competitive algorithm (ICA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf algorithm (GWO), Particle Swarm Optimization (PSO) and Genetic algorithm (GA). Analyzing the findings, it is shown that the CLA algorithm is able to provide more accurate results than other well-known meta-heuristic ones. Also, those results applied to famous unimodal and multimodal benchmarks show CLA is efficient in improving accuracy as well as computational speed.

关键词： Optimization meta-heuristic algorithm competitive learning algorithm

来源：评论

学校读者我要写书评

暂无评论

A New meta-heuristic Optimization algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System

引用

JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2016年第4期27卷 419-440页

作者： Razmjooy, Navid Khalilpour, Mohsen Ramezani, Mehdi Univ Tafresh Dept Elect Engn Tafresh Iran Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran Univ Tafresh Dept Math Tafresh Iran

This paper presents a new optimization algorithm based on human society's intelligent contests. FIFA World Cup is an international association football competition competed by the senior men's national teams. This contest is one of the most significant competitions among the humans in which people/teams try hard to overcome the others to earn the victory. In this competition there is only one winner which has the best position rather than the others. This paper introduces a new technique for optimization of mathematic functions based on FIFA World Cup competitions. The main difficulty of the optimization problems is that each type of them can be interpreted in a specific manner. World Cup Optimization (WCO) algorithm has a number of parameters to solve any type of problems due to defined parameters. For analyzing the system performance, it is applied on some benchmark functions. It is also applied on an optimal control problem as a practical case study to find the optimal parameters of PID controller with considering to the nominal operating points , changes of the AVR system. The main objective of the proposed system is to minimize the steady-state error and also to improve the transient response of the AVR system by optimal PID controller. Optimal values of the PID controller which are achieved by WCO algorithm are then compared with particle swarm optimization and imperialist competitive algorithm in different situations. Finally for illustrating the system capability against the disturbance, it is applied on a generator with disturbance on it and the results are compared by the other algorithms. The simulation results show the excellence of WCO algorithm performance into the nature base and other competitive algorithms.

关键词： meta-heuristic algorithm Optimization Continuous optimization problems World Cup Optimization (WCO) Genetic algorithm PSO algorithm ICA AVR system Optimal PID control

来源：评论

学校读者我要写书评

暂无评论

Artificial lemming algorithm: a novel bionic meta-heuristic technique for solving real-world engineering optimization problems

引用

ARTIFICIAL INTELLIGENCE REVIEW 2025年第3期58卷 1-113页

作者： Xiao, Yaning Cui, Hao Abu Khurma, Ruba Castillo, Pedro A. Southern Univ Sci & Technol Ctr Control Sci & Technol Shenzhen 518055 Peoples R China Dalian Univ Technol Inst Adv Control Technol Dalian 116024 Peoples R China Al Balqa Appl Univ Al Huson Univ Coll Informat Technol Dept Irbid 19117 Jordan Univ Granada Dept Comp Engn Automat & Robot Granada 18071 Spain

The advent of the intelligent information era has witnessed a proliferation of complex optimization problems across various disciplines. Although existing meta-heuristic algorithms have demonstrated efficacy in many scenarios, they still struggle with certain challenges such as premature convergence, insufficient exploration, and lack of robustness in high-dimensional, nonconvex search spaces. These limitations underscore the need for novel optimization techniques that can better balance exploration and exploitation while maintaining computational efficiency. In response to this need, we propose the Artificial Lemming algorithm (ALA), a bio-inspired metaheuristic that mathematically models four distinct behaviors of lemmings in nature: long-distance migration, digging holes, foraging, and evading predators. Specifically, the long-distance migration and burrow digging behaviors are dedicated to highly exploring the search domain, whereas the foraging and evading predators behaviors provide exploitation during the optimization process. In addition, ALA incorporates an energy-decreasing mechanism that enables dynamic adjustments to the balance between exploration and exploitation, thereby enhancing its ability to evade local optima and converge to global solutions more robustly. To thoroughly verify the effectiveness of the proposed method, ALA is compared with 17 other state-of-the-art meta-heuristic algorithms on the IEEE CEC2017 benchmark test suite and the IEEE CEC2022 benchmark test suite. The experimental results indicate that ALA has reliable comprehensive optimization performance and can achieve superior solution accuracy, convergence speed, and stability in most test cases. For the 29 10-, 30-, 50-, and 100-dimensional CEC2017 functions, ALA obtains the lowest Friedman average ranking values among all competitor methods, which are 1.7241, 2.1034, 2.7241, and 2.9310, respectively, and for the 12 CEC2022 functions, ALA again wins the optimal Friedman average ran

关键词： Artificial lemming algorithm (ALA) meta-heuristic algorithm Swarm intelligence Global optimization Engineering problems

来源：评论

学校读者我要写书评

暂无评论

Domain Knowledge Used in meta-heuristic algorithms for the Job-Shop Scheduling Problem:Review and Analysis

引用

Tsinghua Science and Technology 2024年第5期29卷 1368-1389页

作者： Lin Gui Xinyu Li Qingfu Zhang Liang Gao State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Computer Science City University of Hong KongHong KongChina

meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable *** combining domain knowledge of the specific optimization problem,the search efficiency and quality of meta-heuristic algorithms can be significantly improved,making it crucial to identify and summarize domain knowledge within the *** this paper,we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem(JSP).Firstly,this paper delves into the importance of domain knowledge in optimization algorithm *** that,the development of different methods for the JSP are reviewed,and the domain knowledge in it for meta-heuristic algorithms is summarized and *** of this domain knowledge are analyzed,showing it is indispensable in ensuring the optimization performance of meta-heuristic ***,this paper analyzes the relationship among domain knowledge,optimization problems,and optimization algorithms,and points out the shortcomings of the existing research and puts forward research *** paper comprehensively summarizes the domain knowledge in the JSP,and discusses the relationship between the optimization problems,optimization algorithms and domain knowledge,which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future.

关键词： domain knowledge job-shop scheduling problem meta-heuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

APPLICATION OF HYBRID meta-heuristic algorithm FOR ASSIGNING REAL-TIME TASKS TO HETEROGENEOUS PROCESSORS 6

APPLICATION OF HYBRID META-HEURISTIC ALGORITHM FOR ASSIGNING...

引用

6th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Marimuthu, Poongothai Arumugam, Rajeswari Coimbatore Inst Technol Dept Elect & Commun Engn Coimbatore 641014 Tamil Nadu India

ISBN: (纸本)9781479979837

Heterogeneous multiprocessor system has the capability for providing low cost and high performance. Nevertheless, in order to take advantage of computing power of heterogeneous system, it is essential to use an efficient scheduling algorithm for task allocation to the available processors. This research mainly focuses on the development of task assignment algorithm for heterogeneous multiprocessor system. Assigning real-time tasks in heterogeneous multiprocessor are in general a challenging problem and NP hard. In this paper, Hybrid Max-Min Ant colony optimization algorithm (H-MMAS) is proposed to solve the real-time task assignment problem in heterogeneous multiprocessor. The objective of the proposed algorithm is to get a feasible task assignment solution, and to optimize energy consumption of every feasible task assignment solution. The performance of the proposed H-MMAS algorithm has been tested for consistent and inconsistent heterogeneous multiprocessor systems. Experimental comparisons with existing Modified BPSO algorithm demonstrate the effectiveness of the proposed H-MMAS algorithm.

关键词： ACO Real-time task assignment Heterogeneous processors meta-heuristic algorithm Local search algorithms

来源：评论

学校读者我要写书评

暂无评论

Power Allocation with meta-heuristic algorithms for Indoor MIMO-NOMA Based VLC Systems

引用

WIRELESS PERSONAL COMMUNICATIONS 2024年第1期136卷 617-630页

作者： Altunbas, Yasin Turk, Kadir Erzincan Binali Yildirim Univ Dept Elect & Elect Engn TR-24002 Erzincan Turkiye Karadeniz Tech Univ Dept Elect & Elect Engn TR-61080 Trabzon Turkiye

Visible light communication (VLC) is a form of short-range wireless optical communication that uses light in the visible band for both communication and illumination. The limited switching frequencies of light-emitting diodes (LEDs), which are mostly preferred as transmitters in VLC systems, adversely affect the speed of communication. Non-orthogonal multiple access (NOMA) as an access method, and multiple-input multiple-output (MIMO) are used in VLC systems to raise the sum rate. Several methods have been proposed in the literature to determine needed power allocation coefficients for indoor MIMO-NOMA based VLC systems. In this study, meta-heuristic optimization algorithms are proposed to determine the power allocation coefficient that are subject to various constraints including fairness. The numeric results demonstrate the superiority of meta-heuristic methods over sum rate performance. In cases where the user signal strength values differ and the number of users increases, or in other words, the solution becomes more complex, the sum rate acquired by traditional methods reduces, whereas the sum rate using the suggested methods almost does not change and stays high.

关键词： Visible light communication (VLC) meta-heuristic algorithm Non-orthogonal multiple access (NOMA) Multiple-input multiple-output (MIMO)

来源：评论

学校读者我要写书评

暂无评论

Reservoir evaporation prediction with integrated development of deep neural network models and meta-heuristic algorithms (Case study: Dez Dam)

引用

EARTH SCIENCE INFORMATICS 2025年第2期18卷 1-23页

作者： Farzad, Reza Sharafati, Ahmad Ahmadi, Farshad Hosseini, Seyed Abbas Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran Shahid Chamran Univ Ahvaz Dept Hydrol & Water Resources Engn Ahvaz Iran

To manage water resources, reservoirs, and basins, the accurate prediction of evaporation is one of the most significant and necessary processes for the sustainability and preservation of water reserves. On the other hand, climate change can affect the evaporation parameter, so the prediction of evaporation from the reservoirs of dams is vital for managing water resources. development of deep models and their integration to optimization algorithms for modeling and climate variable prediction has drawn a lot of attention in recent years .The present study integrated Multi-Verse Optimizer (MVO), Black Hole (BH), and Marine Predator (MPA) meta-heuristic algorithms with a Long-Term Memory (LSTM) deep neural network model to develop models to forecast the reservoir's evaporation rate at Dez Dam, Khuzestan Province, Iran. The data series has been prepared in two formats: satellite data and modified data from 1981 to 2018 for 37 years. This study also used the meteorological parameters of surface pressure, maximum, minimum, and average temperatures, the speed of wind, relative humidity, and evaporation. Different scenarios have been investigated to examine the effectiveness of various input parameters on the prediction accuracy of the LSTM, LSTM-MPA, LSTM-MVO, and LSTM-BH models. Predicted results of the models showed that the integrated LSTM-MPA-Satellite model with evaluation criteria of RMSE, MAE, KGE, and WI of 64.3540, 48.7079, 0.8164, and 0.8266, respectively, outperforms other meta-heuristic models. The LSTM-BH-Correct is also introduced as the second-best model with almost similar results of 64.4992, 48.8199, 0.8146, and 0.8262. temperature, Relative humidity, and wind speed are the main variables influencing evaporation. Additionally, the candlestick chart shows that the variables have a more regular state when using meta-heuristic techniques as opposed to using the LSTM model alone, indicating a better fit to the data and improved model optimization. Based on the

关键词： Evaporation prediction Dam reservoir LSTM deep neural network meta-heuristic 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：