检索结果-内蒙古大学图书馆

Test case optimization using grey wolf algorithm

SOFTWARE QUALITY JOURNAL 2025年第2期33卷 1-30页

作者： Kumari, Srishti Jindal, Shweta Sharma, Arun Indira Gandhi Delhi Tech Univ Women Comp Sci & Engn Artificial Intelligence New Delhi 110006 India Indira Gandhi Delhi Tech Univ Women Informat Technol New Delhi 110006 India

Testing is an important part of any software development process. Increased effectiveness of software testing and reduction in cost can be achieved by reordering called Test Case optimization (TCO). This paper proposes a new algorithm for multi-objective test case optimization (TCP) using the Grey Wolf optimization (GWO) algorithm and current approach is based on optimizing maximum fault coverage with minimum runtime of test cases. The GWO is an optimization method inspired by nature, based on the mechanism of how grey wolf hunts. To achieve the research purpose, a comprehensive literature review of the GWO algorithm, software testing, and related optimization techniques was conducted. Based on that findings, this paper represents a new algorithm, named GWO_TCO, which combines GWO with the traveling salesman problem concept to optimize test cases in software regression testing. The proposed algorithm has been evaluated and analyzed over seventeen open-source problems along with one benchmark flex dataset. The experimentation is aimed at reduction in size and time of the resultant test suite. Also, 72% average percentage correctness have been achieved. Further, It was found that the average % fault detection of each program was nearly 85% or more going upto 100% fault coverage. Henceforth, GWO_TCO reduces the number of test cases required for software testing while maintaining a high level of early fault detection in a relatively short time, making it practical for real-world software testing scenarios. The studies' findings demonstrated that, in terms of the number of defects found and the effectiveness of the testing as a whole, the proposed algorithm performed better than almost all of the conventional approaches. providing valuable insights for software developers and testers to improve their testing processes and reduce testing time and costs.

关键词： Software testing Test case optimization Meta-heuristics optimization Test case selection Test case prioritization Regression techniques GWO

来源：评论

学校读者我要写书评

暂无评论

Optimal allocation of distributed generation for power flow enhancement in distribution network

引用

OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第6期44卷 3168-3184页

作者： Jayaraman Panneerselvam, Srividhya Krishnamoorthi, Yogitha Ramamoorthy, Suresh Selvaraj, Jaisiva Arunai Engn Coll Dept EEE Tiruvannamalai Tamilnadu India Arunai Engn Coll Dept ECE Tiruvannamalai Tamilnadu India Sreenivasa Inst Technol & Management Studies Dept EEE Chittoor Andhra Prades India Sri Krishna Coll Technol Dept EEE Coimbatore Tamilnadu India

This article articulates the enhancement of voltage profiles in transmission systems by reducing the voltage deviation and system power losses. Electric power flows uniformly throughout Regional Distribution Networks in the power grid station, resulting in low voltage consistency, significant voltage dips and power losses in the distribution network, highlighting high R/X ratios even with a heavy load or power failure issues, despite installing distributed generation in the radial distribution system for enhancing voltage profile in the grid network. The meta-heuristic optimization algorithm plays a vital role in determining optimal location of DG for attaining the objective of research. In the practical network, a single objective optimization strategy cannot be employed to solve of power system optimization problems of all kinds. Hence multi-objective function needs to be addressed. The prime task of this research is to investigate and allocate an optimal location to connect the Distributed Generation, with evaluation of the best DG configuration, with minimized power losses and improved voltage profile of the distribution network using the Hummingbird optimization Algorithm. A standard IEEE 37-bus Radial Distribution system is employed as the test bus system to test the performance and effectiveness of the optimization technique. To show the stiffness of the proposed algorithm the results are compared with various optimization approaches that employ similar objective functions.

关键词： distributed generation hummingbird optimization algorithm power loss radial distribution system voltage profile

来源：评论

学校读者我要写书评

暂无评论

Optimal shape design of a class of permanent magnet motors in a multiple-objectives context

引用

COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2022年第6期41卷 1994-2009页

作者： Di Barba, Paolo Mognaschi, Maria Evelina Petkovska, Lidija Cvetkovski, Goga Vladimir Univ Pavia Dept Elect Comp & Biomed Engn Pavia Italy Ss Cyril & Methodius Univ Fac Elect Engn & Informat Technol Skopje North Macedonia

Purpose This paper aims to deal with the optimal shape design of a class of permanent magnet motors by minimizing multiple objectives according to an original interpretation of Pareto optimality. The proposed method solves a many-objective problems characterized by five objective functions and five design variables with evolution strategy algorithms, classically used for single- and multi-objective (two objective functions) optimization problems. Design/methodology/approach Two approaches are proposed in the paper: the All-objectives (AO) and the Many-objectives (MO) optimization approach. The former is based on a single-objective optimization of a preference function, i.e. a normalized weighted sum. In contrast, in the MO a multi-objective optimization algorithm is applied to the minimization of a weight-free preference function and simultaneously to a maximization of the distance of the current solution from the prototype. The optimizations are based on an equivalent circuit model of the Permanent Magnet (PM) motor, but the results are assessed by means of finite element analyses (FEAs). Findings An extensive study of the solutions obtained by means of the different optimization approaches is provided by means of post-processing analyses. Both the approaches find non-dominated solutions with respect to the prototype that are substantially improving the initial solution. The points of strength along with the weakness points of each solution with respect to the prototype are analysed in depth. practical implications The paper gives a good guide to the designers of electric motors, focussed on a shape design optimization. Originality/value Considering simultaneously five objective functions in an automated optimal design procedure is challenging. The proposed approach, based on a well-known and established optimization algorithm, but exploiting a new concept of degree of conflict, can lead to new results in the field of automated optimal design in a many-objective co

关键词： Shape design Permanent magnet motor Stochastic algorithms All-objective optimization Many-objective optimization Permanent magnet machine multiobjective optimization Shape optimization Design optimization methodology

来源：评论

学校读者我要写书评

暂无评论

Adopting a Bi-level optimization Method for the Freight Transportation Problem: A multi-objective Programming Approach

引用

TRANSPORTATION RESEARCH RECORD 2023年第2期2677卷 490-504页

作者： Rahiminia, Shahab Mehrabi, Amir Aghaee, Mohsen Pourseyed Jamili, Amin Iran Univ Sci & Technol Sch Ind Engn Tehran Iran Iran Univ Sci & Technol Sch Railway Engn Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

Nowadays, various companies find it a crucial task to elevate their distribution system efficiency to survive in what is an intensely competitive environment. Freight transportation via road systems may not always be a practical option for the shipper because of financial, social, and environmental aspects. So, in addition to the road system, the shipper can benefit greatly from the rail system. Thus, taking advantage of different transportation modes can help shippers to tackle, to some extent, arising issues. In this paper, we aim at investigating a freight transportation network including rail and road transportation modes in which the shipper's choice affects the decisions deeply. Given that this problem as a bi-level programming model, the rail operator acts as a leader and the shipper is treated as a follower. Once the rail operator reveals the rail transportation price, an appropriate response is received from the shipper and this pattern is repeated until an equilibrium is achieved. From the sustainability view, a multi-objective mixed-integer programming model is developed to simulate their interactions considering a sustainable triple bottom line. Employing the Karush-Kuhn-Tucker approach, we converted the formulated bi-level programming model into a single one. Several numerical examples are investigated to evaluate the validity of the developed model and approaches. Different in-depth sensitivity analyses are carried out and the impacts of several factors, such as line and train capacity, on the rail operator and shipper's decisions are investigated. The results reveal that the rail operator could increase their market share by a wise long-term investment in the rail infrastructure.

关键词： freight transportation network mode choice bi-level programming stackelberg game multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Joint subcarrier, power and beamforming optimization for MIMO-OFDM dual function radar and communication

引用

DIGITAL SIGNAL PROCESSING 2025年 163卷

作者： Wen, Rong Sun, Gaoyuan Li, Qiang Tang, Youxi Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China Chengdu Technol Univ Chengdu 610031 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Tianfu Jiangxi Lab Chengdu 641419 Peoples R China

Dual Function Radar and Communication (DFRC) has emerged as a promising technology for achieving multifunctionality and high spectral efficiency within a single platform. This study examines a multi-Input multi-Output (MIMO) and Orthogonal Frequency Division multiplexing (OFDM)-based DFRC system, which transmits confidential information to a communication user while simultaneously detecting a potential malicious target. To ensure security, the principles of physical-layer security are employed. The secrecy capacity and mutual information for target parameter estimation are used as Quality of Service (QoS) metrics for communications and target sensing, respectively. A resource allocation problem is formulated, encompassing subcarrier assignment, power allocation, and beamforming, with the objective of minimizing the total transmit power while satisfying QoS requirements for both target detection and secure communication. This resource allocation problem is a mixed-integer nonlinear program (MINLP) and is NP-hard. To address this, we propose two pragmatic approaches: the greedy approach and the penalized block-coordinate descent (PBCD) approach. Specifically, in the greedy approach, discrete subcarrier assignment is initially fixed, followed by optimization of power allocation and beamforming;subsequently, subcarrier assignment is updated in a greedy manner. In contrast, the PBCD approach transforms the MINLP into a purely continuous optimization problem and cyclically optimizes the resource variables. It is proven that the PBCD approach converges to an epsilon-Karush-Kuhn-Tucker (KKT) solution. Simulation results indicate the superiority of the PBCD approach over the greedy method and existing designs.

关键词： DFRC OFDM Secrecy capacity Penalized block-coordinate descent

来源：评论

学校读者我要写书评

暂无评论

Effective and Imperceptible Adversarial Textual Attack Via multi-objectivization

引用

ACM Transactions on Evolutionary Learning and optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

INTELLIGENT ROUTE PLANNING METHOD FOR UAV BASED ON SWARM INTELLIGENCE AND DEEP LEARNING TECHNOLOGY

引用

COMPUTING AND INFORMATICS 2024年第4期43卷 874-899页

作者： Yang, Jian Huang, Xuejun Natl Univ Def Technol Coll Elect Engn Hefei 230031 Anhui Peoples R China

Due to its potential applications in numerous industries, Unmanned Aerial Vehicles (UAVs) have gained considerable attention recently. UAV networks that are autonomous and decentralized have various practical uses, such as in disaster recovery, environmental monitoring, and security surveillance. Due to frequent route distractions and traffic congestion at high node speeds, the performance of routing systems in these networks drops considerably. UAVs with a mission to gather sensory data from various sources require meticulous route planning to decrease traffic congestion effectively. Due to flight time, range, and coverage area limitations, efficient route planning is crucial for maximizing the efficiency of UAV data collection. Optimal route planning and a delicate balancing act between these critical parameters are two of the biggest obstacles in sensory data gathering. This study presents a new method for dealing with these issues by developing an Intelligent Route Planning for Sensory Data Collection (IRP-SDC) system to optimize autonomous UAV route planning with congestion-aware modelling by considering time, distance, and area coverage limits. The IRP-SDC framework uses multi-objective Grey Wolf optimization and Deep Q-Learning (MOGWO-DQL) for smart UAV route planning. The MOGWO algorithm, developed after observing the hunting techniques of grey wolves, can perform a worldwide search, which helps determine the most efficient paths to take after gathering information. DQL, on the other hand, has adaptive learning capabilities that can modify the UAV's flight path in response to alterations in its external environment. The suggested framework combines the two techniques to maximize the usefulness of UAVs in gathering sensory data. Extensive trials were carried out to prove the efficacy of the proposed technique. The IRP-SDC system beats previous approaches concerning time, distance, and area coverage by providing an ideal route for a UAV to acquire sensory data

关键词： Autonomous UAVs intelligent route planning sensory data swarm intelligence Deep-Q-Learning multi-objective grey wolf optimization congestion-aware modeling

来源：评论

学校读者我要写书评

暂无评论

multi-objective approaches to portfolio optimization with market impact costs

引用

MEMETIC COMPUTING 2022年第4期14卷 411-421页

作者： Wang, Hongze Li, Xuerong Hong, Wenjing Tang, Ke Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Market impact costs are important factors to portfolio management, which always lead to adverse price fluctuations in trading. As the practical trading volume becomes increasingly large, the Problem of Portfolio optimization with Market Impact Costs (MICPOP) has become more important. Traditional MICPOPs involve seeking an optimal allocation of capital to a limited number of assets with respect to either additional constraints or a weighted sum of objectives (net return, investment risk). We suggest solving MICPOPs with multi-objective Evolutionary Algorithms (MOEAs). Specifically, we formulate MICPOPs as a bi-objective optimization problem. The advantages of MOEAs over state-of-the-art single objective approaches to MICPOPs will be shown through empirical studies. Our study has revealed that a well-known MOEA, namely Nondominated Sorting Genetic Algorithm II (NSGA-II), fails to provide satisfactory solution quality sometimes. Hence, a memetic MOEA for Portfolio optimization with Market Impact costs (POMI-MOEA), which inherits the global search capability of NSGA-II while introducing a new local search operator, is proposed and evaluated in this paper. Comprehensive experimental studies on 11 portfolio cases have shown the superiority of POMI-MOEA over NSGA-II and other two MOEAs for MICPOPs.

关键词： Portfolio optimization Market impact cost multi-objective optimization Evolutionary computation Memetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Population-based and hybrid heuristic approaches for the bi-objective sustainable multi-vehicle traveling purchaser problem

引用

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2025年

作者： Cheaitou, Ali Hamdan, Sadeque Quteineh, Heba Alsyouf, Imad Shikhli, Amir Univ Sharjah Coll Engn Sharjah U Arab Emirates Bangor Univ Bangor Business Sch Bangor LL57 2DG Gwynedd Wales Univ Sharjah Res Inst Sci & Engn Sharjah U Arab Emirates

This article examines a retailer's challenge in selecting sustainable suppliers, assigning orders, routing multiple capacitated vehicles to collect purchased products within a restricted travel distance, and choosing the vehicles' speed levels. It presents a bi-objective mixed-integer linear programming model that allows varying speed levels between arcs of the asymmetric network connecting suppliers and between vehicles on the same arc. Suppliers have different capacities and can supply various products to meet deterministic demands, with total supply capacity exceeding demand for each product. The retailer aims to select the optimal suppliers to minimize variable and fixed procurement costs, fuel and transportation costs, and CO2 social costs while maximizing the social and environmental worth of the procured goods. The study extends the pollution routing problem and the traveling purchaser problem by integrating environmental and social sustainability and accounting for the social cost of CO2 emissions. An exact approach and two suggested heuristic algorithms are used to solve the model. A comprehensive numerical analysis demonstrates that the proposed solution algorithms reliably approach the optimum within a practical amount of computation time for the instances considered.

关键词： Supplier selection sustainability social cost of CO2 speed optimization population-based heuristic hybrid heuristic

来源：评论

学校读者我要写书评

暂无评论

A review of optimization techniques and algorithms used for FRP applications in civil engineering

引用

Journal of Engineering and Applied Science 2023年第1期70卷 61页

作者： Ramadan, Amany S. Elgendi, Elbadr O. Alexandria Egypt

optimization problems aim to minimize or maximize an objective function while fulfilling related constraints. This objective function may be a single or multi-objective optimization. Many studies have been conducted on using these optimization problems in civil and construction engineering, especially for the various machine learning techniques and algorithms that have been developed for fiber reinforced polymer (FRP) applications in the rehabilitation and design of RC structures. FRP is considered the most effective and superior technique for strengthening and retrofitting due to its significant benefits over traditional methods, which have numerous drawbacks, as well as the importance of structural strengthening as a cost-effective and practical option. In this research, an insight into how to apply algorithms and machine learning approaches to optimize FRP applications in civil and construction engineering is presented, as well as a detailed analysis of the various optimization strategies used and their findings. A total of 18 case studies from previous research were discussed and critically evaluated, and they were categorized into six groups according to the algorithm or machine learning technique utilized. Based on the case studies investigated in this study, the genetic algorithm was found to be the optimal algorithm utilized for optimizing FRP applications. The result of this research provides a useful guideline for future researchers and specialists. © 2023, The Author(s).

关键词： Machine learning

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：