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Enhancing bug allocation in software development: a multi-criteria approach using fuzzy logic and evolutionary algorithms

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2111页

作者： Gupta, Chetna Gupta, Varun Jaypee Inst Informat Technol Noida India Univ Alcala Dept Econ & Business Adm Alcala De Henares Madrid Spain GISMA Univ Appl Sci Multidisciplinary Res Ctr Innovat SMEs MrciS Potsdam Germany

A bug tracking system (BTS) is a comprehensive data source for data -driven decision-making. Its various bug attributes can identify a BTS with ease. It results in unlabeled, fuzzy, and noisy bug reporting because some of these parameters, including severity and priority, are subjective and are instead chosen by the user ' s or developer ' s intuition rather than by adhering to a formal framework. This article proposes a hybrid, multi-criteria fuzzy-based, and multi -objective evolutionary algorithm to automate the bug management approach. The proposed approach, in a novel way, addresses the trade-offs of supporting multi-criteria decision-making to (a) gather decisive and explicit knowledge about bug reports, the developer ' s current workload and bug priority, (b) build metrics for computing the developer ' s capability score using expertise, performance, and availability (c) build metrics for relative bug importance score. Results of the experiment on fi ve open -source projects (Mozilla, Eclipse, Net Beans, Jira, and Free desktop) demonstrate that with the proposed approach, roughly 20% of improvement can be achieved over existing approaches with the harmonic mean of precision, recall, f-measure, and accuracy of 92.05%, 89.04%, 90.05%, and 91.25%, respectively. The maximization of the throughput of the bug can be achieved effectively with the lowest cost when the number of developers or the number of bugs changes. The proposed solution addresses the following three goals: (i) improve triage accuracy for bug reports, (ii) differentiate between active and inactive developers, and (iii) identify the availability of developers according to their current workload.

关键词： evolutionary algorithms Bug tracking system Software development

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A Systematic Review of Multi-Objective evolutionary algorithms Optimization Frameworks

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PROCESSES 2024年第5期12卷 869页

作者： Patrausanu, Andrei Florea, Adrian Neghina, Mihai Dicoiu, Alina Chis, Radu Lucian Blaga Univ Sibiu Dept Comp Sci & Elect Engn 4 Emil CioranStr Sibiu 550025 Romania

The study of evolutionary algorithms (EAs) has witnessed an impressive increase during the last decades. The need to explore this area is determined by the growing request for design and the optimization of more and more engineering problems in society, such as highway construction processes, food and agri-technologies processes, resource allocation problems, logistics and transportation systems, microarchitectures, suspension systems optimal design, etc. All of these matters refer to specific highly computational problems with a huge design space, hence the obvious need for evolutionary algorithms and frameworks, or platforms that allow for the implementing and testing of such algorithms and methods. This paper aims to comparatively analyze the existing software platforms and state-of-the-art multi-objective optimization algorithms and make a review of what features exist and what features might be included next as further developments in such tools, from a researcher's perspective. Additionally, it is essential for a framework to be easily extendable with new types of problems and optimization algorithms, metrics and quality indicators, genetic operators or specific solution representations and results analysis and comparison features. After presenting the most relevant existing features in these types of platforms, we suggest some future steps and the developments we have been working on.

关键词： multi-objective optimization evolutionary algorithms frameworks software platforms

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ALMO: Active Learning-Based Multi-Objective Optimization for Accelerating Constrained evolutionary algorithms

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APPLIED SCIENCES-BASEL 2024年第21期14卷 9975页

作者： Singh, Karanpreet Kapania, Rakesh K. Virginia Polytech Inst & State Univ Kevin T Crofton Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA

In multi-objective optimization, standard evolutionary algorithms, such as NSGA-II, are computationally expensive, particularly when handling complex constraints. Constraint evaluations, often the bottleneck, require substantial resources. Pre-trained surrogate models have been used to improve computational efficiency, but they often rely heavily on the model's accuracy and require large datasets. In this study, we use active learning to accelerate multi-objective optimization. Active learning is a machine learning approach that selects the most informative data points to reduce the computational cost of labeling data. It is employed in this study to reduce the number of constraint evaluations during optimization by dynamically querying new data points only when the model is uncertain. Incorporating machine learning into this framework allows the optimization process to focus on critical areas of the search space adaptively, leveraging predictive models to guide the algorithm. This reduces computational overhead and marks a significant advancement in using machine learning to enhance the efficiency and scalability of multi-objective optimization tasks. This method is applied to six challenging benchmark problems and demonstrates more than a 50% reduction in constraint evaluations, with varying savings across different problems. This adaptive approach significantly enhances the computational efficiency of multi-objective optimization without requiring pre-trained models.

关键词： active learning multi-objective optimization multi-disciplinary optimization machine learning evolutionary algorithms optimization query learning optimal experimental design

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Optimization of Steel Jackets to Support Offshore Wind Turbines Using evolutionary algorithms

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JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING-TRANSACTIONS OF THE ASME 2024年第2期146卷 022001-022001页

作者： Cruz, Rodrigo Oliveira Duarte, Grasiele Regina de Lima, Beatriz Souza Leite Pires Jacob, Breno Pinheiro Fed Univ Rio Janeiro Postgrad Inst COPPE UFRJCivil Engn Dept Cidade UnivAve Pedro CalmonS-N BR-21941596 Rio De Janeiro RJ Brazil

This paper presents an optimization tool for jacket structures to support Offshore Wind Turbines (OWTs). The tool incorporates several combinations of optimization algorithms and constraint-handling techniques (CHTs): Genetic Algorithm;Differential Evolution (DE);Tournament Selection Method;Multiple Constraint Ranking (MCR);Adaptive Penalty Method, and Helper-and-Equivalent Optimization. The objective function regards the minimization of the jacket weight;the design variables are the diameter and thickness of the tubular members. The constraints are related to natural frequencies and Ultimate Limit State criteria. The candidate solutions are evaluated by full nonlinear time-domain Finite Element coupled analyses. To assess the optimization algorithms and CHTs, a case study is presented for the standardized OWT/jacket structure from the Offshore Code Comparison Collaboration Continuation project. First, a numerical model is built and validated, in terms of masses, natural frequencies, and vibration modes;then, this model is employed to run the optimization tool for all combinations of optimization algorithms and CHTs. The results indicate that, while all methods lead to feasible optimal solutions that comply with the constraints and present considerable weight reductions, the best performer is the combination of the DE algorithm with the MCR constraint-handling technique.

关键词： structural optimization evolutionary algorithms offshore jackets offshore wind turbines design of offshore structures ocean energy technology

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A Comparative Study of Optimal PV Allocation in a Distribution Network Using evolutionary algorithms

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ENERGIES 2024年第2期17卷 511页

作者： Bai, Wenlei Zhang, Wen Allmendinger, Richard Enyekwe, Innocent Lee, Kwang Y. Baylor Univ Sch Engn & Comp Sci Waco TX 76706 USA Baylor Univ Hankamer Sch Business Waco TX 76706 USA Univ Manchester Alliance Manchester Business Sch Manchester M15 6PB England

The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward-backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case.

关键词： distributed energy resources evolutionary algorithms optimal PV allocation PV penetration

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EvoLearner: Learning Description Logics with evolutionary algorithms 22

EvoLearner: Learning Description Logics with Evolutionary Al...

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31st ACM Web Conference (WWW)

作者： Heindorf, Stefan Blubaum, Lukas Dusterhus, Nick Werner, Till Golani, Varun Nandkumar Demir, Caglar Ngomo, Axel-Cyrille Ngonga Univ Paderborn Dept Comp Sci DICE Grp Paderborn Germany

ISBN: (纸本)9781450390965

Classifying nodes in knowledge graphs is an important task, e.g., for predicting missing types of entities, predicting which molecules cause cancer, or predicting which drugs are promising treatment candidates. While black-box models often achieve high predictive performance, they are only post-hoc and locally explainable and do not allow the learned model to be easily enriched with domain knowledge. Towards this end, learning description logic concepts from positive and negative examples has been proposed. However, learning such concepts often takes a long time and state-of-the-art approaches provide limited support for literal data values, although they are crucial for many applications. In this paper, we propose EvoLearner-an evolutionary approach to learn concepts in ALCQ(D), which is the attributive language with complement (ALC) paired with qualified cardinality restrictions (Q) and data properties (D). We contribute a novel initialization method for the initial population: starting from positive examples, we perform biased random walks and translate them to description logic concepts. Moreover, we improve support for data properties by maximizing information gain when deciding where to split the data. We show that our approach significantly outperforms the state of the art on the benchmarking framework SML-Bench for structured machine learning. Our ablation study confirms that this is due to our novel initialization method and support for data properties.

关键词： Description Logics evolutionary algorithms Machine Learning

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Intelligent mixture optimization for stabilized soil containing solid waste based on machine learning and evolutionary algorithms

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CONSTRUCTION AND BUILDING MATERIALS 2024年 445卷

作者： Wang, Junzhi Chen, Geng Chen, Yonghui Ye, Zi Lin, Minguo Su, Ruobin Hu, Nan Hohai Univ Minist Educ Geomech & Embankment Engn Key Lab Nanjing 210098 Peoples R China Hohai Univ Res Inst Geotech Engn Nanjing 210098 Peoples R China Hohai Univ Suzhou Res Inst Suzhou 215131 Peoples R China

The usage of industrial solid waste to improve soil for road materials has attracted widespread attention. In addition to mechanical performance, economic and environmental factors gain increasing concern during road construction. This study proposes an intelligent mixture design method based on machine learning and multiobjective evolutionary algorithms. Six machine learning models for predicting California bearing ratio value of stabilized soil were developed and evaluated utilizing a dataset containing 403 samples. With the best prediction model, three multi-objective evolutionary algorithms were adopted to optimize the three objective functions including CBR, material cost, and carbon emission. The multi-objective optimization model established by combining Extreme Gradient Boosting and Non-dominated Sorting Genetic Algorithm-II successfully found the Pareto front for the three-objective optimization problem under various decision preferences. Eventually, the weights of each optimization objective were determined with subjective-objective combination assignment, which was coupled with the Technique for Order Preference by Similarity to Ideal Solution method to determine the optimal solution. The results suggest the importance of determining the optimal solution based on the demand of the decision maker and data information. The proposed framework can comprehensively consider the mechanical, economic, and environmental objectives, achieving the multi-objective optimization design of stabilized soil. This study offers practical value for the application of solid waste stabilized soils in road construction and provides an alternative towards the intelligent utilization of solid waste material.

关键词： Multi-objective optimization Machine learning evolutionary algorithms Solid waste Stabilized soil

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Airfoil Optimization Using Deep Learning Models and evolutionary algorithms for the Case Large-Endurance UAVs Design

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DRONES 2024年第10期8卷 570-570页

作者： Minaev, Evgenii Pioquinto, Jose Gabriel Quijada Shakhov, Valentin Kurkin, Evgenii Lukyanov, Oleg Samara Natl Res Univ 34 Moskovskoe Shosse Samara 443086 Russia

This article presents the development of the AZTLI-NN network and the evaluation of this network as a set of evolutionary algorithms in airfoil optimization tasks. AZTLI-NN has the characteristic of predicting the aerodynamic coefficients of the airfoils in the form of images (graphs of the aerodynamic coefficients as a function of the angle of attack) from parameter vectors corresponding to the parameterization method CST. This feature allows the network to achieve good performance when generalizing the predictions of the aerodynamic coefficients, being on par with neural networks that have the aerodynamic coefficients encoded in the form of structured data, and has the ability to handle a wide range of usage airfoils in general aviation. In addition, a case of how AZTLI-NN together with an adaptive evolutionary algorithm and population size reduction methods achieve good performance in finding the airfoil that provides the highest possible endurance value is shown, so this work is considered as an option in the early stages of the design for the selection of airfoils in the design of large-endurance UAVs.

关键词： airfoil optimization OpenFOAM neural networks evolutionary algorithms method CST large-endurance UAV

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Towards Explainable Real Estate Valuation via evolutionary algorithms

Towards Explainable Real Estate Valuation via Evolutionary A...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Angrick, Sebastian Bals, Ben Hastrich, Niko Kleissl, Maximilian Schmidt, Jonas Doskoc, Vanja Molitor, Louise Friedrich, Tobias Katzmann, Maximilian Univ Potsdam Hasso Plattner Inst Potsdam Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781450392372

Human lives are increasingly influenced by algorithms, which therefore need to meet higher standards not only in accuracy but also with respect to explainability. This is especially true for high-stakes areas such as real estate valuation. Unfortunately, the methods applied there often exhibit a trade-off between accuracy and explainability. One explainable approach is case-based reasoning (CBR), where each decision is supported by specific previous cases. However, such methods can be wanting in accuracy. The unexplainable machine learning approaches are often observed to provide higher accuracy but are not scrutable in their decision-making. In this paper, we apply evolutionary algorithms (EAs) to CBR predictors in order to improve their performance. In particular, we deploy EAs to the similarity functions (used in CBR to find comparable cases), which are fitted to the data set at hand. As a consequence, we achieve higher accuracy than state-of-the-art deep neural networks (DNNs), while keeping interpretability and explainability. These results stem from our empirical evaluation on a large data set of real estate offers where we compare known similarity functions, their EA-improved counterparts, and DNNs. Surprisingly, DNNs are only on par with standard CBR techniques. However, using EA-learned similarity functions does yield an improved performance.

关键词： evolutionary algorithms case-based reasoning neural networks real estate valuation

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EVOVAQ: evolutionary algorithms-based toolbox for VAriational Quantum circuits

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SOFTWAREX 2024年 26卷

作者： Acampora, Giovanni Gutierrez, Carlos Cano Chiatto, Angela Hidalgo, Jose Manuel Soto Vitiello, Autilia Univ Naples Federico II Dept Phys Ettore Pancini Naples Italy Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Granada Dept Comp Engn Automat & Robot Granada Spain

evolutionary algorithms (EAs) are becoming increasingly popular for training Variational Quantum Circuits (VQCs) due to their ability to conserve quantum resources. However, there is currently a lack of user-friendly tools for implementing this approach. To address this issue, this paper proposes EVOVAQ, a Python-based framework designed to simplify the use of EAs for training VQCs. EVOVAQ seamlessly integrates evolutionary computation with quantum libraries such as Qiskit, making it easy to use for both quantum computing and EAs communities. Furthermore, EVOVAQ's scalability enables the development of customized solutions, promoting innovation in the quantum computing field.

关键词： evolutionary algorithms Python package Quantum computing Quantum machine learning Variational quantum algorithms

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