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Searching for Strongly Subsuming Higher Order Mutants by Applying multi-objective optimization algorithm 1

3rd International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA)

作者： Quang Vu Nguyen Madeyski, Lech Wroclaw Univ Technol Fac Comp Sci & Management Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (数字)9783319179964

ISBN: (纸本)9783319179964;9783319179957

Higher order mutation testing is considered a promising solution for overcoming the main limitations of first order mutation testing. Strongly subsuming higher order mutants (SSHOMs) are the most valuable among all kinds of higher order mutants (HOMs) generated by combining first order mutants (FOMs). They can be used to replace all of its constituent FOMs without scarifying test effectiveness. Some researchers indicated that searching for SSHOMs is a promising approach. In this paper, we not only introduce a new classification of HOMs but also new objectives and fitness function which we apply in multi-objective optimization algorithm for finding valuable SSHOMs.

关键词： Mutation Testing Higher Order Mutation Higher Order Mutants Strongly Subsuming multi-objective optimization algorithm

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Design of fuzzy controllers for Brushless DC motors based on multi-objective optimization algorithm

Design of fuzzy controllers for Brushless DC motors based on...

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Chinese Automation Congress (CAC)

作者： Zhang Lei Li Pengfei Henan Univ Sci & Technol Sch Elect Engn Luoyang Peoples R China

ISBN: (纸本)9781467371896

a design method of adaptive fuzzy controllers for Brushless DC motors by applying an improved multi-objective optimization algorithm is proposed. The proposed optimization algorithm can optimize and determine the fuzzy rules and membership functions parameters of fuzzy controllers simultaneously in the optimizing, and reconcile the demands of more than one conflicting dynamic and stationary performances. In order to maintain the diversity of individuals to avoid premature mature in the proposed multi-objective optimization algorithm, a new enhancement mechanism is proposed. Simulation experimental results show the designed adaptive fuzzy controllers have stronger robustness and resisting disturbance ability. At the same time, the designed fuzzy adaptive controllers have satisfactory dynamic and stationary performances for Brushless DC motors speed control.

关键词： multi-objective optimization algorithm Brushless DC motor fuzzy control adaptive control

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Surrogate-assisted decomposition multi-objective evolutionary algorithm for parameters optimization in polyester fiber polymerization process

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2025年 213卷 243-260页

作者： Zhang, Peng Fei, Bo Bi, Jinmao Wang, Ming Zhao, Chuncai Zhang, Jie Donghua Univ Inst Artificial Intelligence Shanghai Engn Res Ctr Ind Big Data & Intelligent S Shanghai Peoples R China Donghua Univ Coll Mech Engn Shanghai Peoples R China Huzhou Zhongshi Technol Co Ltd Xinfengming Grp Zhejiang Peoples R China

This study presents the development of a two-stage adaptive decomposition multi-objective evolutionary algorithm (TSAMOEAD) designed to optimize quality control in industrial aggregation processes, such as polyester fiber production. To address the time delay issue in quality indicator detection caused by production continuity, we first introduce an improved Informer model. This model predicts multiple quality indicators in real time from multivariate time series data, serving as a surrogate for process parameter optimization. Additionally, we enhance the CCF lag time estimation method to account for time delays in quality control, ensuring that adjustments to process parameters are made within the available time frame. In the second part of the study, we develop a two-stage adaptive decomposition-based multi-objective evolutionary algorithm to optimize polymerization process parameters. The first stage involves rapidly approximating the Pareto front using specific weight vectors and genetic operators. The second stage enhances solution diversity and convergence through the use of adaptive weight vectors and operators. To simplify the selection of optimal solutions from the Pareto front, we propose an indicator-based screening method that efficiently identifies the most suitable adjustment schemes. Experimental results demonstrate that our approach accurately predicts quality indicators and provides effective parameter adjustment strategies that meet production requirements.

关键词： Polyester fiber polymerization process multi-objective optimization algorithm Time lag analysis

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A dynamic combination algorithm based scenario construction theory for mine water-inrush accident multi-objective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期238卷

作者： Li, Wei Wang, Linbing Ye, Zhoujing Liu, Yalin Wang, Yongming Univ Sci & Technol Beijing Natl Ctr Mat Serv Safety Beijing 100083 Peoples R China Univ Georgia Sch Environm Civil Agr & Mech Engn Athens GA 30602 USA Chinese Acad Governance Beijing 100089 Peoples R China

Mine safety is a crucial aspect of national infrastructure. In recent years, there have been frequent mine safety accidents in China, and the proportion of mine water-inrush accidents (MWAs) is high among them. It causes significant challenges for governments and mine managers. The emergency response of MWAs is a multi-scale expert decision-making system that involves considerable cost, time, and energy consumption. The Mine Accidents Central Control Office is constituted of mine managers and government officials, and it lacks scientific validity in analyzing the risk correlation only based on sensory-historical experience. It can lead to deviations in emergency response capability and cause unnecessary waste of costs, time, and energy. This study introduces scenario construction theory and multi-attribute dynamic combination algorithms to address this systemic challenges based on the data-driven model. Firstly, the dataset is structured by the induction method based on historical MWAs from 2003 to 2023 in China, and it is divided into nine accident attributes and three calculated categories. Secondly, the scenario parameters are selected with bottom-line thinking and correlation analysis of accident attributes. Thirdly, the scenario queue model and scenario disposal model are constructed based on the scenario construction theory with the dynamic combination algorithm;a systemic mathematical model is established to optimize the multi-objective (cost, time, and energy consumption) in emergency response. Finally, a real metal mine is selected for model simulation, based on data results to analyze algorithm variable reflected handle measure. This study provides a novel approach and scientific reference to improve emergency plans in mine safety management. It enhances the effectiveness and timeliness of emergency response in the MWAs.

关键词： Digital mine Scenario construction theory Emergency management Mine water-inrush accidents multi-objective optimization algorithm

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Comparative Review of multi-objective optimization algorithms for Design and Safety optimization in Electric Vehicles

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IEEE ACCESS 2024年 12卷 146376-146396页

作者： Dharma, I. Gede S. S. Setiawan, Rachman Inst Teknol Bandung Fac Mech & Aerosp Engn Doctoral Program Mech Engn Bandung 40132 West Java Indonesia Inst Teknol Bandung Fac Mech & Aerosp Engn Bandung 40132 West Java Indonesia

Despite the widespread use of established optimization algorithms like Non-Dominated Sorting Genetic algorithm-II (NSGA-II), Non-Dominated Sorting Genetic algorithm-III (NSGA-III), and multi-objective Evolutionary algorithm based on Decomposition (MOEA/D) in real-world engineering optimization problems, newer algorithms such as Two-Stage NSGA-II (TS-NSGA-II), Dynamic Constrained NSGA-III (DCNSGA-III), MOEA/D with Virtual objective Vectors (MOEA/D-VOV), Large-Scale Evolutionary multi-objective optimization Assisted by Directed Sampling (LMOEA-DS), and Super-Large-Scale multi-objective Evolutionary algorithm (SLMEA) remain underexplored in the context of Battery Electric Vehicle (BEV) safety, particularly in optimizing complex, non-linear, and constrained multi-objective problems like crashworthiness and thermal management. This study addresses this gap by comparing these newer algorithms against traditional methods using a newly introduced benchmark problem focused on BEV battery protection (RWMOP-BEV). The design problem aimed to maximize energy absorption during impact, enhance crash force efficiency, and optimize temperature difference, all while adhering to design space and operational constraints. The comparative results, based on four performance indicators-hypervolume (HV), inverted generational distance (IGD), averaged Hausdorff distance (Delta(p)) , and spread-reveal that SLMEA emerged as the best algorithm, not only for RWMOP-BEV but also across other benchmark sets, including DTLZ problems and other real-world multi-objective optimization problems.

关键词： Batteries optimization Accidents Benchmark testing Thermal management Electric vehicles Heuristic algorithms Measurement Heating systems Evolutionary computation multi-objective optimization algorithm battery electric vehicle (BEV) safety crashworthiness thermal management

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multi-objective Scheduling optimization of Prefabricated Components Production Using Improved Non-Dominated Sorting Generic algorithm II

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BUILDINGS 2025年第5期15卷 742-742页

作者： Zhao, Yishi Du, Shaokang Tu, Ming Ma, Haichuan Shang, Jianga Xiang, Xiuqiao China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Minist Educ Engn Res Ctr Nat Resource Informat Management & Di Wuhan 430074 Peoples R China China Construct Third Engn Buerau Grp Intelligent Construct Syst Res Inst Wuhan 430075 Peoples R China

The traditional construction industry is characterized by high energy consumption and significant carbon emissions, primarily due to its reliance on on-site manual labor and wet operations, which are not only low in mechanization but also result in low material efficiency and substantial construction waste. Prefabricated construction offers a new solution with its efficient production methods, significantly enhancing material utilization and construction efficiency. This paper focuses on the production scheduling optimization of prefabricated components. The production scheduling directly affects the construction speed and cost of prefabricated buildings. Given the complex modeling and numerous constraints faced by the production of prefabricated components, we propose an improved Non-dominated Sorting Genetic algorithm II (NSGA-II) for multi-objective optimization. The algorithm incorporates adaptive operators and greedy concepts for local search, enhancing solution exploration and diversity. We segment the production of prefabricated components into six stages, analyzing dependencies and constraints, and form a comprehensive scheduling model with objectives of minimizing contract penalties, storage costs, and production time. Extensive experiments demonstrate that the improved NSGA-II provides a more balanced and larger set of solutions compared to baseline algorithms, offering manufacturers a wider range of options. This research contributes to the optimization of production scheduling in the prefabricated construction industry, supporting coordinated, sustainable, automated, and transparent production environments.

关键词： offsite manufacturing prefabricated component scheduling model multi-objective optimization algorithm

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Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm

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CONSTRUCTION AND BUILDING MATERIALS 2024年 418卷

作者： Dong, Chaowei Zhou, Nan Ferro, Giuseppe Andrea Yan, Hao Xu, Jianfei Wang, Haodong Liu, Sixu Zhang, Zhanguo China Univ Min & Technol Sch Mines Xuzhou 221116 Peoples R China China Univ Min & Technol Artificial Inlligence Res Inst Xuzhou 221116 Peoples R China State Key Lab Coal Resources & Safe Min Xuzhou 221116 Peoples R China Politecn Torino Dept Struct Geotech & Bldg Engn I-10129 Turin Italy China Shenhua Energy Co Ltd Beijing 102209 Peoples R China China Univ Min & Technol Sch Mines State Key Lab Coal Resources & Safe Min Xuzhou 221116 Peoples R China

The gangue grouting and backfilling for the subsequent space after coal mining can effectively address solid waste disposal and protect the ecological environment in the mining area. In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm;B:2.36 - 1.18 mm;C: 1.18 - 0.6 mm;D: 0.6 - 0.3 mm;E: 0.3 0.15 mm;F: 0.15 - 0.075 mm;G: 0.075 - 0 mm) and water contents (H) were considered, and the fluidity, bleeding rate and uniaxial compressive strength (UCS) of the pure gangue backfilling slurry (PGBS) were determined as response objectives. Subsequently, fifty-two sets of proportion optimization schemes of the PGBS were generated by a D-optimal mixture design, the individual and interactive effects of influencing factors on the response objectives were studied, and the optimal proportion of the PGBS was globally obtained based on a multi-objective optimization algorithm. The variance (ANOVA) results show that the P-values of the prediction model for fluidity, bleeding rate, and UCS are less than 0.0001 (much less than 0.05), their lack of fit P-values are greater than 0.05, and their R-2 reaches 0.9225, 0.8780 and 0.8378, respectively. The three response-targeted prediction models are statistically significant and highly effective, demonstrating superior fitting to experimental data and accurate forecasting of the PGBS performance. The gangue sand with a particle size of 0.075 - 0 mm (G) and water content (H) have decisive effects on the three response objectives, and G exhibits a similar effect to that of cementitious powder in PGBS. Additionally, the two influencing factors A-F, A-H, D-G, and E-G have significant interactive effects on fluidity, and D-H has a significant interactive effect on bleeding rate. The optimal proportion of PGBS obtained by the multi-objective optimization algorithm is A: B: C: D: E: F: G: H = 0.15: 0.13: 0.04: 0.06: 0.099: 0.1: 0.184: 0.237. The results of repeated experiments suggest that the error between the pred

关键词： Engineering performance multi-objective optimization algorithm Mixture design Pure gangue backfilling slurry Utilization of coal gangue

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multi-objective Bacterial Foraging optimization algorithm Based on Effective Area in Cognitive Emergency Communication Networks

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China Communications 2021年第12期18卷 252-269页

作者： Shibing Zhang Xue Ji Lili Guo Zhihua Bao School of Information Science and Technology Nantong UniversityNantong 226019China Xinglin College Nantong UniversityNantong 226008China

Cognitive emergency communication net-works can meet the requirements of large capac-ity,high density and low delay in emergency *** paper analyzes the properties of emergency users in cognitive emergency communi-cation networks,designs a multi-objective optimiza-tion and proposes a novel multi-objective bacterial foraging optimization algorithm based on effective area(MOBFO-EA)to maximize the transmission rate while maximizing the lifecycle of the *** the algorithm,the effective area is proposed to prevent the algorithm from falling into a local optimum,and the diversity and uniformity of the Pareto-optimal solu-tions distributed in the effective area are used to eval-uate the optimization ***,the dynamic preservation is used to enhance the competitiveness of excellent individuals and the uniformity and diversity of the Pareto-optimal solutions in the effective ***,the adaptive step size,adaptive moving direc-tion and inertial weight are used to shorten the search time of bacteria and accelerate the optimization *** simulation results show that the pro-posed MOBFO-EA algorithm improves the efficiency of the Pareto-optimal solutions by approximately 55%compared with the MOPSO algorithm and by approx-imately 60%compared with the MOBFO algorithm and has the fastest and smoothest convergence.

关键词： wireless communications emergency communications cognitive radio networks multi-objective optimization algorithm effective areas self-adaption

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Evaluation on Software Performance optimization Based on multi-objective optimization algorithms

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Procedia Computer Science 2023年 228卷 864-871页

作者： Meiqin Li Shandong Vocational College of Science and Technology Weifang Shandong China

The tasks undertaken by software in various tasks include precise positioning of computational targets, utilization of hardware resources, and so on. Therefore, updating software performance is an essential and important step. However, currently, software iteration in computers is not smooth sailing. Therefore, this article aimed to study the problem of software performance optimization through the direction of objective optimization algorithms. This article used the method of evaluating parameter optimization results to conclude that multi-objective optimization algorithms have stronger computational power in models in other fields. The optimization results of the target software are statistically analyzed through web log mining, and the Chebyshev method is also used to decompose one of the classic multi-objective algorithms, MOEA/D. Finally, the optimization approach was divided into two types: multi-objective optimization algorithm and single objective optimization algorithm. Two other computers with identical configurations were selected to use these two algorithms for experiments. The multi-objective optimization algorithm was set as the experimental group, while the single objective optimization algorithm was set as the control group. After performing several software optimization operations, the results showed that the software performance optimization operation based on the multi-objective optimization algorithm improved the software running speed much higher than the control group executing the single objective optimization algorithm. The final conclusion is that in the research of software performance optimization, MOEA/D in multi-objective optimization algorithms not only defeats single objective optimization algorithms, but also is a highly efficient means in itself.

关键词： multi-objective optimization algorithm Research on Software Performance optimization Evaluation of Parameter optimization Results Web Log Mining Chebyshev Method

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Mesh antenna cable net form finding optimization by an improved NSGA-II

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2025年第2期53卷 1144-1175页

作者： Meng, Nanyu Shi, Zhiqi Zhu, Hao Zhou, Qinghua Sichuan Univ Sch Aeronaut & Astronaut Chengdu Peoples R China Sichuan Univ Dept Mech & Engn Sci Chengdu Peoples R China

The tension uniformity and surface accuracy of mesh antenna cable net are two well-accepted evaluation criteria for form finding. Traditional form finding techniques often rely on single-objective optimization and weighting factors. Although the Non-dominated Sorting Genetic algorithm II (NSGA-II) can address the limitation, there are still challenges of wide distribution and insufficient precision of solutions. In the present work, a novel multi-objective optimization method based on an improved NSGA-II is introduced to conduct mesh antenna cable net form finding optimization. Based on the preference region (PR), a self-adaptive penalty function is proposed. The function is designed to steer the optimization process toward the PR, and enhancing the precision of the solutions. The extent of penalization for an individual solution depends on its positioning relative to the PR and is modified by a penalty coefficient that adapts dynamically to the population number within the PR. Additionally, a form finding method considering flexible frames is developed. The initial population for the flexible frame optimization is sourced from results of rigid frames, with the maximum truss node position error as the convergence criterion. Benchmark tests and case studies confirm that the improved NSGA-II enhances distributivity and convergence, while also providing form-finding results with higher surface accuracy and more uniform tension distribution.

关键词： Form finding cable net mesh antenna multi-objective optimization algorithm surface accuracy tension distribution uniformity

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