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IEEE Congress on evolutionary Computation (CEC)

作者： Zeng, Yujiao Sun, Yanguang Automat Res & Design Inst Met Ind State Key Lab Hybrid Proc Ind Automat Syst & Equi Beijing Peoples R China

ISBN: (纸本)9781479914883

The optimal reactive power dispatch (ORPD) problem is formulated as a complex multiobjective optimization problem, involving nonlinear functions, continuous and discrete variables and various constraints. Recently, multiobjective evolutionary algorithms (MOEAs) and multiobjective particle swarm optimization (MOPSO) have received a growing interest in solving the multiobjective optimization problems. In this paper, MOPSO, and two highly competitive algorithms of MOEAs, that is, nondominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm (SPEA2) are presented for solving the ORPD problem. Moreover, a mixed-variable handling method and an effective constraint handling approach are employed to deal with various types of variables and constraints. The proposed algorithms are evaluated on the standard IEEE 30-bus and 118-bus test systems. In addition, several multiobjective performance metrics are employed to compare these algorithms with respect to convergence, diversity, and computational efficiency. The results show the effectiveness of MOEAs and MOPSO for solving the ORPD problem. Furthermore, the comparison results indicate that MOPSO generally outperforms other algorithms for ORPD and has a great potential in dealing with large-scale optimal power flow problems.

关键词： optimal reactive power dispatch evolutionary algorithms MOPSO multiobjective optimization

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DoFA: Adversarial examples detection for SAR images by dual-objective feature attribution

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 255卷

作者： Zhang, Yu Zeng, Guo-Qiang Chen, Min-Rong Geng, Guang-Gang Weng, Jian Lu, Kang-Di Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Jinan Univ Natl Joint Engn Res Ctr Network Secur Detect & Pro Guangzhou 510632 Peoples R China Wenzhou Univ Natl Local Joint Engn Lab Digitalize Elect Design Wenzhou 325035 Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510632 Peoples R China Zhejiang Univ Inst Cyber Syst & Control Natl Lab Ind Control Technol Hangzhou 310027 Peoples R China

Synthetic aperture radar (SAR) classification models based on convolutional neural networks have high accuracy, but the models' security is still threatened by adversarial examples. The high threat of adversarial examples derives from the invisible noise that can cause feature changes within the model. Among many adversarial examples detection methods, feature attribution that is sensitive to feature changes performs well in feature analysis. Unfortunately, the existing feature attribution-based detection methods cannot balance the computational efficiency and detection performance well due to the size and speckle noise of the images in SAR adversarial examples detection. In this work, we propose the Dual-objective Feature Attribution (DoFA) method by using the feature attribution scan block to find the suitable scan granularity. The DoFA method formulates the SAR adversarial examples detection issue as a dual-objective optimization problem and takes the number of subsamples generated by feature analysis and the area under curve (AUC) value of the logistic regression model as the objective functions while the feature scan block's size, stride, padding, and the number of selected model layers are the decision variables. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to search for the scan block parameters with Pareto optimality so that the DoFA method can automatically obtain the best feature analysis granularity in different scenarios. The experimental results on the FUSAR-Ship dataset have shown that the proposed DoFA method has a higher AUC value under five adversarial attacks and a smaller number of subsamples than the existing adversarial examples detection method.

关键词： Synthetic aperture radar Adversarial examples detection Feature attribution Dual-objective optimization evolutionary algorithms Logistic regression

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Preserving privacy in association rule mining using multi-threshold particle swarm optimization

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INFORMATION SCIENCES 2025年 692卷

作者： Aljehani, Shahad Alotaibi, Youseef Umm Al Qura Univ Coll Comp Dept Comp Sci & Artificial Intelligence Mecca 21955 Saudi Arabia Umm Al Qura Univ Coll Comp Dept Software Engn Mecca 21955 Saudi Arabia

Healthcare data has become a powerful resource for generating insights that drive medical research. Association Rule Mining (ARM) techniques are widely used to identify relationships among diseases, treatments, and symptoms. However, sensitive information is often exposed, creating significant privacy challenges, particularly when data is integrated from multiple sources. Although Privacy-Preserving Association Rule Mining (PPARM) methods have been developed to address these issues, most rely on a single, predefined Minimum Support Threshold (MST) that is inflexible in adapting to diverse rule patterns. In this study, a Multi-Threshold Particle Swarm Optimization for Association Rule Mining (MPSO4ARM) model is introduced, integrating the Apriori and Particle Swarm Optimization (PSO) algorithms to perform data mining while protecting sensitive rules. A novel approach is employed by the proposed model to dynamically adjust the MST, allowing for more adaptive and effective privacy preservation. The MPSO4ARM model adjusts the MST on-the-fly based on rule length, improving its ability to safeguard sensitive data across various datasets. The proposed model was evaluated on the Chess, Mushroom, Retail, and Heart Disease datasets. The experimental results showed that the MPSO4ARM model outperforms traditional Apriori and conventional PSO algorithms, achieving higher fitness values and reducing side effects such as Hiding Failure (HF) and Missing Cost (MC), particularly in the Heart Disease and Mushroom datasets. Although the dynamic MST function introduces a moderate increase in computational runtime compared to Apriori and conventional PSO, this trade-off between execution time and enhanced privacy protection is considered acceptable, given the model's substantial improvements in data utility and rule sanitization.

关键词： Privacy-preserving Multi-threshold association rule mining Data sanitization evolutionary algorithms Particle swarm optimization Sensitive rule protection

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An Evolutional Topology Optimization Method Based on Kernel Level Set Function

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IEEE ACCESS 2025年 13卷 47181-47200页

作者： Sato, Takahiro Watanabe, Kota Igarashi, Hajime Muroran Inst Technol Grad Sch Engn Muroran Hokkaido 0508585 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600814 Japan

Topology optimizations involving evolutionary algorithms are promising approaches to solve practical engineering design problems, since their use of derivation-free algorithms makes them applicable to any design problem. In some evolutional topology optimization methods, structures are determined through the use of spatially smooth parametric functions. This article, first, presents a new parametric level set function using kernel functions as a general formulation of spatially smooth function-based methods. It is called the kernel level set function, which is used to determine the distributions of materials in the design region. By using the evolutionary algorithm to modify the profile of the kernel level set function, topology optimizations can be realized for various design problems, and the shape expression ability of the kernel level set function can be customized by changing the kernel functions. Second, a multi-objective formulation considering an objective and structural complexity is introduced, and an optimization algorithm specialized for solving it is proposed. The combination of the kernel level set function and the present optimization algorithm makes it possible to solve various topology optimization problems, and Pareto fronts with respect to target performance and structural complexity can be obtained. The present method is applied to three topology optimization problems. The numerical results are used to examine the characteristics of the present method for various kernel functions. Furthermore, the present method is applied to an optimization problem of an interior permanent magnet motor for electric vehicles. It is shown that the present method can solve practical design problems that have various objectives and constraint conditions, thanks to the use of a derivation-free evolutionary algorithm.

关键词： Level set Kernel Topology evolutionary computation Linear programming Complexity theory Electric machines Finite element analysis Shape Reviews CMA-ES electric machine evolutionary algorithms topology optimization

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Multiple optimal solutions in synthesis, design and operation optimization of a marine energy system with the differential evolution algorithm

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OCEAN ENGINEERING 2025年 328卷

作者： Dugast, Florian Benac, Stephane Marty, Pierre Chesse, Scal Nantes Univ Ecole Cent Nantes CNRS LHEEA UMR 6598 F-44000 Nantes France Technocampus Ocean Naval Grp CEMEP Bouguenais France

This article deals with the optimization of a ship energy system on multiple levels (synthesis, design and operation). These complex problems often induce many local optima, making it difficult to obtain reliable optimization results. A clustering method based on the absence or presence of components in the architecture coupled with an evolutionary algorithm (i.e Differential Evolution) is proposed to tackle this issue. The method enables multiple optimal solutions to be identified for a real-world optimization problem. The reference ship for this study is a destroyer but the use of the Admiralty coefficient in the model description makes the algorithm easily adaptable for any kind of ships. The specific fuel consumption is pre-calculated for groups of components to alleviate the computational cost of the optimization problem. Two numerical cases are computed representing the ship energy system with or without a heat recovery and a heat creation system to illustrate the capabilities of the method. The objective in both cases is to minimize the weight of the ship (fuel consumption + components weights) for a given mission profile. The influence of the clustering technique, population size and repeatability of the differential algorithm is also investigated to assess the reliability of the method.

关键词： Ship architecture design Synthesis optimization Design optimization evolutionary algorithms

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Two-stage bidirectional coevolutionary algorithm for constrained multi-objective optimization

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SWARM AND evolutionary COMPUTATION 2025年 92卷

作者： Zhao, Shulin Hao, Xingxing Chen, Li Yu, Tingfeng Li, Xingyu Liu, Wei Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China Leiden Univ Leiden Inst Adv Comp Sci NL-2333 CA Leiden Netherlands

Objective optimization and constraint satisfaction are two primary and conflicting tasks in solving constrained multi-objective optimization problems (CMOPs). To better trade off them, this paper proposes a two-stage bidirectional coevolutionary algorithm, termed C-TBCEA, for constrained multi-objective optimization. It consists of two stages, with each concentrating on specific targets, i.e., the first stage primarily focuses on objective optimization while the second stage focuses on constraint satisfaction by employing different evolutionary strategies at each stage. Via the synergy of the two stages, a dynamic trade-off between objective optimization and constraint satisfaction can be achieved, thus overcoming the distinctive challenges that may be encountered at different stages of evolution. In addition, to take advantage of both feasible and infeasible solutions, we employ two populations, i.e., the main population that stores the non-dominated feasible solutions and the archive population that maintains the informative infeasible solutions, to prompt the bidirectional coevolution of them. To validate the effectiveness of the proposed C-TBCEA, experiments are carried out on 6 CMOP test suites and 17 real-world CMOPs. The results demonstrate that the proposed algorithm is very competitive with 9 state-of-the-art constrained multi-objective optimization evolutionary algorithms (CMOEAs).

关键词： Constrained multi-objective optimization Two-stage Bidirectional coevolution evolutionary algorithms

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Analysis of evolutionary algorithms using Multi-Objective Parameter Tuning 14

Analysis of Evolutionary Algorithms using Multi-Objective Pa...

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16th Genetic and evolutionary Computation Conference (GECCO)

作者： Ugolotti, Roberto Cagnoni, Stefano Univ Parma Dept Informat Engn I-43100 Parma Italy

ISBN: (纸本)9781450326629

evolutionary algorithms (EAs) and other metaheuristics are greatly affected by the choice of their parameters, not only as regards the precision of the solutions found, but also for repeatability, robustness, speed of convergence, and other properties. Most of these performance criteria are often conflicting with one another. In our work, we see the problem of EAs' parameter selection and tuning as a multi-objective optimization problem, in which the criteria to be optimized are precision and speed of convergence. We propose EMOPaT (evolutionary Multi-Objective Parameter Tuning), a method that uses a well-known multi-objective optimization algorithm (NSGA-II) to find a front of non-dominated parameter sets which produce good results according to these two metrics. By doing so, we can provide three kinds of results: (i) a method that is able to adapt parameters to a single function, (ii) a comparison between Differential Evolution (DE) and Particle Swarm Optimization (PSO) that takes into consideration both precision and speed, and (iii) an insight into how parameters of DE and PSO affect the performance of these EAs on different benchmark functions.

关键词： Parameter Tuning evolutionary algorithms Multi-objective Optimization Parameter Analysis

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QRS Detection Optimization in Stress Test Recordings Using evolutionary algorithms 41

QRS Detection Optimization in Stress Test Recordings Using E...

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41st Computing in Cardiology Conference (CinC)

作者： Hernando, David Bailon, Raquel Almeida, Rute Hernandez, Alfredo Univ Zaragoza IIS Aragon Inst Engn Res I3A Biomed Signal Interpretat & Computat Simulat BSIC Zaragoza Spain CIBER BBN Zaragoza Spain Univ Porto Fac Ciencias P-4100 Oporto Portugal CMUP Oporto Portugal INSERM U1099 F-35000 Rennes France Univ Rennes 1 LTSI F-35000 Rennes France

ISBN: (纸本)9781479943463

QRS detection in exercise stress test recordings remains a challenging task, because they are highly non-stationary and contaminated with noises, such as large baseline wander and muscular noise, among others. The aim of this work is to find an optimal set of parameters for QRS detection in very noisy ECG signals, such as those acquired during stress tests. Parameter optimization was addressed by an evolutionary algorithm. A training database was created using 48 ECG recordings with reference QRS complexes. Each ECG recording is artificially contaminated with 3 types of real noise. A cost function combining the detection error probability, the mean detection jitter, and its standard deviation was defined, in order to obtain a quantitative performance evaluation of the detector. Evaluation was performed on an exercise stress test database composed of 54 real ECG recordings, with annotated QRS. The detector was configured with default parameter values, and also with the optimal values obtained from the evolutionary algorithm. The QRS detector with its optimized parameters showed a mean improvement of 4.6% compared to its performance with the default parameters. Furthermore, the use of optimized parameters led to at least the same performance than the initial parameters for all records, and the improvement was higher (up to 19.36 %) in noisy records, demonstrating the advantages of the optimized parameters in noisy environments.

关键词： electrocardiography medical signal detection muscle optimisation signal denoising QRS detection optimization baseline wander cost function evolutionary algorithms mean detection jitter muscular noise noises noisy ECG signals parameter optimization stress test recordings Databases Detectors Electrocardiography Noise Noise measurement Optimization Stress

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A genetic iterated greedy algorithm for the blocking flowshop to minimize total earliness and tardiness

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年第5期35卷 2161-2174页

作者： Prata, Bruno de Athayde Fuchigami, Helio Yochihiro Univ Fed Ceara Dept Ind Engn Fortaleza CE Brazil Univ Fed Sao Carlos Prod Engn Dept Sao Carlos Brazil

An important and realistic class of scheduling problems is considered in this paper: the total earliness and tardiness minimization in the blocking flowshop, where there is no intermediate buffer between machines. Blocking occurs when a completed item or product remains on the machine until the next machine is available. We proposed a new hybrid evolutionary algorithm: the Genetic Iterated Greedy Algorithm (GIGA). In our innovative solution approach, a genetic algorithm presents a hybrid crossover based on the Iterated Greedy metaheuristic. The hybrid crossover considers the Hamming distance as an indicator of the diversity of the current population. In the first generations, the crossover will adopt larger values for the destruction parameter, and this value is gradually reduced throughout the search process. Our proposal is compared to four competitive metaheuristics reported for earliness and tardiness flowshop. Two performance indicators are considered: the Average Relative Percentage Deviation (ARPD) and the Success Rate (SR). Based on the statistical analysis of the computational experimentation, our GIGA outperformed all the implemented algorithms of the literature with statistical significance. Concerning the performance indicators, GIGA achieved ARPD = 0.02% and SR = 83.5%, pointing to the superiority of the proposed solution approach.

关键词： Production sequencing Just-in-time Metaheuristics evolutionary algorithms

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Modeling Metal Protein Complexes from Experimental Extended X-ray Absorption Fine Structure using evolutionary algorithms

Modeling Metal Protein Complexes from Experimental Extended ...

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IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

作者： Price, Collin Houghten, Sheridan Vassiliev, Sergey Bruce, Doug Brock Univ St Catharines ON L2S 3A1 Canada

ISBN: (纸本)9781479945351

Experimental extended x-ray absorption fine structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, predicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo Optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proved somewhat successful in structure refinement but have not been successful in finding the global minima. Based on the success of using evolutionary algorithms to overcome local minima issues in other domains, we propose multiple approaches to better predict the structure of metal protein complexes;genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE).

关键词： Molecular Structure evolutionary algorithms Representation Recentering-Restarting Particle Swarm Optimization Extended X-ray Absorption Fine Structure

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