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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

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Physics-informed data-driven modeling approach for commuting-oriented hybrid powertrain optimization

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ENERGY CONVERSION AND MANAGEMENT 2024年 299卷

作者： Lei, Nuo Zhang, Hao Li, Rulong Yu, Jun Wang, Hong Wang, Zhi Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Dongfeng Motor Corp Ltd Wuhan 430058 Peoples R China

The physics-informed data-driven approach is instructive in powertrain modeling with high-fidelity, which can be adopted for powertrain optimization to further improve vehicle performances. In this paper, a plug-in hybrid electric vehicle (PHEV) equipped with a novel powertrain is considered as the research object, and a series of vehicle bench experiments are carried out for operative data acquisition. With the collected data, the physicsinformed data-driven models for engine fuel consumption and battery state of health (SOH) loss are proposed. The accuracy and superiority of the proposed model are demonstrated compared with other approaches. In order to realize the commuting-oriented powertrain optimization, the driving cycle representing the commuting scenario is reconstructed based on the collected traffic data in Beijing. The synthesized driving cycle generated by the proposed method can describe the practical application scenario in Beijing within the allowable error range. In addition, the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to further optimize the parameters for the hybrid powertrain according to vehicle performances. The multi-objective optimization is completed with the engine fuel consumption and battery state of health (SOH) loss as targets. The results indicate that the optimized configuration parameters can reduce fuel consumption by 4.85% and battery capacity loss by 3.7% compared with the original vehicle.

关键词： Physics-informed data-driven Hybrid powertrain Vehicle bench experiment Commuting scenario reconstruction Evolutionary optimization

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

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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

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Durability prediction of sustainable marine concrete under freeze-thaw cycles using multi-objective machine learning models

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CASE STUDIES IN CONSTRUCTION MATERIALS 2025年 22卷

作者： Rezzoug, Aissa AlAteah, Ali H. Alinsaif, Sadiq Mostafa, Sahar A. Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Engn Riyadh 11432 Saudi Arabia Univ Hafr Al Batin Coll Engn Dept Civil Engn Hafar al Batin 39524 Saudi Arabia Univ Hafr Al Batin Coll Comp Sci & Engn Hafar Al Batin 39524 Saudi Arabia Beni Suef Univ Fac Engn Dept Civil Engn Bani Suwayf Egypt

Marine and cold-region concrete structures are prone to surface scaling, interior microcracking, and long-term strength degradation due to freeze-thaw cycles (FTCs). Understanding and accurately predicting FTC-induced damage is essential for sustainable and resilient structural design. This study introduces machine learning (ML)-based algorithms for forecasting FTC-induced durability of concrete while simultaneously predicting sustainability metrics such as CO2 emissions and production cost. To enhance accessibility and practical usability, a graphical user interface (GUI) was developed, enabling engineers to input concrete mix parameters and obtain performance predictions without requiring programming knowledge. Four machine learning techniques were utilized: a convolutional neural network (CNN), a genetic algorithm with optimized artificial neural network (GA-ANN), an adaptive neuro-fuzzy inference system (ANFIS), and multi-objective optimization (MOO). The dataset used in this study was compiled from previously published experimental results in the literature, allowing the development of predictive models without the need for new laboratory testing. All models demonstrated strong predictive accuracy (validation R-2 > 0.90), aligning well with observed experimental trends. The MOO model achieved the highest performance with R-2 training = 0.974, R-2 testing = 0.96, MAE = 1.12 MPa, and RMSE = 1.54 MPa, outperforming CNN (R-2 = 0.942 and 0.93), GA-ANN (R-2 = 0.931 and 0.902), and ANFIS (R-2 = 0.905 and 0.939) for training and testing phases, respectively. Evaluation metrics included R-2, MAE, MSE, RMSE, and MARE, with model ranking as follows: MOO > ANFIS > CNN > GA-ANN. The MOO model also achieved the most accurate predictions for CO2 emissions and production cost (R-2 = 0.98 and 0.99, respectively). These findings highlight the reliability of ML approaches in predicting concrete performance under FTC conditions, reinforcing their potential for use in the design of

关键词： multi-objective optimization Freeze-thaw cycles K-fold cross-validation Graphical user interface CO2 emission

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

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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

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

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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

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Effective and Imperceptible Adversarial Textual Attack Via multi-objectivization

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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

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multi-objective sparse synthesis optimization of concentric circular antenna array via hybrid evolutionary computation approach

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期231卷

作者： Li, Jiahui Sun, Geng Wang, Aimin Zheng, Xiaoya Chen, Zhaoyu Liang, Shuang Liu, Yanheng Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Jilin Univ Dept Math Changchun 130012 Peoples R China Northeast Normal Univ Sch Informat Sci & Technol Changchun Peoples R China

Sparse synthesis can reduce the maximum sidelobe level (SLL) and overhead of the concentric circular antenna array (CCAA) by switching off partial array elements. However, previous works have not considered finding a balance between the number of switched-on elements of CCAA and reducing the maximum SLL, or jointly optimizing the excitation current weights of array elements to further suppress the maximum SLL. In this work, we address these challenges by formulating a hybrid multi-objective optimization problem (MOP) with discrete and continuous solutions. To solve this hybrid NP-hard problem, we consider two approaches: the two-step and one-step methods. In the two-step method, we convert the original MOP into two subproblems and propose an enhanced non-dominated sorting genetic algorithm-II (ENSGA-II) and an improved genetic algorithm with new mutation and attraction (IGAnMA) to solve these two subproblems. ENSGA-II and IGAnMA involve the hierarchy mechanism and attraction operator to enhance the solving abilities, respectively. In the one-step method, we propose an improved hybrid non-dominated sorting genetic algorithm-II (IHNSGA-II) with global search and hybrid solution update methods to solve the MOP in one step. Numerical simulation results show that the proposed methods are effective and stable for the sparse synthesis of CCAA. Additionally, electromagnetic simulation results demonstrate the validity of the proposed approaches in a more practical environment.

关键词： Beam pattern Concentric circular antenna array Evolutionary computation optimization Sidelobe level

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

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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

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multi-objective approaches to portfolio optimization with market impact costs

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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

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