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A Fast Clustering Based evolutionary Algorithm for Super-Large-Scale Sparse Multi-Objective Optimization

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1048-1063页

作者： Ye Tian Yuandong Feng Xingyi Zhang Changyin Sun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China School of Computer Science and Technology Anhui UniversityHefei 230601China School of Artificial Intelligence Anhui UniversityHefei 230601China School of Automation Southeast UniversityNanjing 210096China

During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable ***,due to the stochastic search strategies,the performance of most EAs deteriorates drastically when handling a large number of decision *** tackle the curse of dimensionality,this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal *** proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution,and provides a fast clustering method to highly reduce the dimensionality of the search *** importantly,all the operations related to the decision variables only contain several matrix calculations,which can be directly accelerated by *** existing EAs are capable of handling fewer than 10000 real variables,the proposed algorithm is verified to be effective in handling 1000000 real ***,since the proposed algorithm handles the large number of variables via accelerated matrix calculations,its runtime can be reduced to less than 10%of the runtime of existing EAs.

关键词： evolutionary computation fast clustering sparse multi-objective optimization super-large-scale optimization

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Pareto Optimization for Subset Selection via Steering Mutation With Theoretical Guarantee

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IEEE computationAL INTELLIGENCE MAGAZINE 2025年第2期20卷 80-94页

作者： Zhang, Lei Wang, Zhanpeng Yang, Haipeng Cheng, Fan Anhui Univ Hefei 230601 Peoples R China

Subset selection, which refers to the selection of a finite number of variables to optimize a given objective function, is a fundamental problem in various applications. Among the existing algorithms for solving this problem, evolutionary algorithms (EAs) based on Pareto optimization have demonstrated good performance in acquiring high-quality subsets with a theoretical guarantee. However, most existing EAs ignore the importance of variables in designing evolutionary operators (for example, mutations), which makes the search inefficient. To fill this gap, this study proposes a steering mutation-based evolutionary algorithm called SMSS for subset selection, where the importance of variables is fully utilized to ensure both the effectiveness and theoretical guarantee of the algorithm. Specifically, a novel steering mutation operator is designed in SMSS to effectively select items of high importance and discard those of low importance in the Pareto optimization process, which provides the generation of final subsets with better quality. In addition, it is proved that SMSS can attain a (1-e(-gamma))-optimal polynomial-time approximation guarantee in scenarios where the objective function exhibits monotonicity. Extensive experiments on two subset selection applications (unsupervised feature selection and sparse regression) demonstrate the superiority of the proposed algorithm over state-of-the-art algorithms.

关键词： evolutionary computation Pareto optimization Linear programming Approximation algorithms Feature extraction

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Harnessing memetic algorithms: a practical guide

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TOP 2025年 1-30页

作者： Cotta, Carlos Univ Malaga ETSI Informat Dept Lenguajes & Ciencias Comp Campus TeatinosOff 3-2-49 E-29071 Malaga Spain Univ Malaga ITIS Software Ada Byron Res BldgC Arquitecto Francisco Penalosa Malaga 29071 Spain

The aim of this work is to provide a didactic approximation to memetic algorithms (MAs) and how to apply these techniques to an optimization problem. MAs are based on the synergistic combination of ideas from population-based metaheuristics and trajectory-based search/optimization techniques. Most commonly, MAs feature a population-based algorithm as the underlying search engine, endowing it with problem-specific components for exploring the search space, and in particular with local-search mechanisms. In this work, we describe the design of the different elements of the MA to fit the problem under consideration, and go on to perform a detailed case study on a constrained combinatorial optimization problem related to aircraft landing scheduling. An outline of some advanced topics and research directions is also provided.

关键词： Memetic algorithms evolutionary computation Local search GRASP Aircraft landing scheduling

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Diagnosing Faults in Power Transformers With Variational Autoencoder, Genetic Programming, and Neural Network

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IEEE ACCESS 2023年 11卷 30529-30545页

作者： Vidal, Juan Ferreira Castro, Adriana Rosa Garcez Fed Univ Para Grad Program Elect Engn BR-66075110 Belem PA Brazil

This work presents a new approach for the diagnosis of incipient faults in power transformers by considering dissolved gas analysis (DGA). A multilayer perceptron (MLP) neural network was trained to diagnose the type of transformer fault. For training and testing of the classifier, data were used from in-service transformers obtained from the IEC TC 10 database and other data obtained from the literature. To address the imbalance of the data from the database adopted and thus improve the generalization power of the classifier, a data augmentation technique based on a variational autoencoder neural network was used. For the selection and extraction of characteristics from the inputs to the classifier, a technique based on genetic programming (GP) is proposed, which allows the creation of a new n-dimensional space of characteristics, providing a greater ability to increase interclass distances and intraclass compaction. For the performance analysis of the proposed classifier, comparisons were made using the classification results obtained through the IEC 60599 conventional fault diagnosis method and other trained MLPs without the use of data augmentation and the proposed characteristics extractor. The results obtained demonstrate the applicability of the proposed methodology for fault diagnosis, with the proposed system obtaining an accuracy of 95.18% in the test basis, which is higher than the results achieved by the other methods used to perform a comparison and analysis of results.

关键词： Databases evolutionary computation Dissolved gas analysis Neural networks Data mining Oil insulation Genetic algorithms IEC 60599 optimization evolutionary computation

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Surrogate-assisted evolutionary optimisation: a novel blueprint and a state of the art survey

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evolutionary INTELLIGENCE 2024年第4期17卷 2213-2243页

作者： Khaldi, Mohammed Imed Eddine Draa, Amer Constantine 2 Univ Abdelhamid Mehri MISC Lab Campus Ali Mendjli Constantine 25000 Algeria Constantine 2 Univ Abdelhamid Mehri NT Fac Campus Ali Mendjli Constantine 25000 Algeria

Surrogate-Assisted evolutionary Optimisation algorithms are a specialized brand of optimisers developed to undertake problems with computationally expensive fitness functions. These algorithms work by building a cheap approximation or model of the exact function and using it in the evaluation of solutions within the optimisation process. This use of modelling techniques within optimisation, while offers a practical reduction in function calls, brings along with it some additional questions. This paper starts with a description of the key elements of surrogate-assisted evolutionary optimisation algorithms as they are outlined throughout the literature, and then, proceeds to rearrange these elements using a novel blueprint of the field. The proposed blueprint can be used to represent any surrogate-assisted evolutionary algorithm in a way that illustrates its principles and components in a non-vague manner. In addition, a survey of the most prominent works in the field is conducted using this novel blueprint. Finally, a number of challenges and perspectives are listed before the paper is concluded.

关键词： evolutionary computation Optimisation Surrogate modelling Fitness approximation Multi-objective optimisation

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evolutionary generative design of supercritical airfoils: an automated approach driven by small data

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COMPLEX & INTELLIGENT SYSTEMS 2024年第1期10卷 1167-1183页

作者： Sun, Kebin Wang, Weituo Cheng, Ran Liang, Yu Xie, Hairun Wang, Jing Zhang, Miao Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Beihang Univ Inst Unmanned Syst Beijing 100191 Peoples R China Shanghai Aircraft Design & Res Inst Shanghai 200436 Peoples R China

Supercritical airfoils are critical components in the design of commercial wide-body aircraft wings due to their ability to enhance aerodynamic performance in transonic flow regimes. However, traditional design methods for supercritical airfoils can be time-consuming and require significant manual effort, not to mention the high cost associated with computational fluid dynamics analysis. To address these challenges, this paper introduces a highly automated approach for supercritical airfoil design, called evolutionary Generative Design (EvoGD). The EvoGD approach is based on the framework of evolutionary computation and employs a series of sophisticated data-driven generative models incorporated with physical information to iteratively refine initial airfoil shapes, resulting in improved aerodynamic performances and reduced constraint violations. Moreover, to speed up the evaluation of the generated airfoils, a series of accurate and efficient data-driven predictors are utilized. The efficacy of the EvoGD approach was demonstrated through experiments on a dataset of 501 supercritical airfoils, including one baseline design and 500 randomly perturbed airfoils. On average, the generated airfoils showed improved performance in terms of buffet lift coefficient, cruise lift-to-drag ratio, and thickness by 5%, 4%, and 1%, respectively. The best generated airfoil outperformed the baseline design in terms of critical buffet lift coefficient and cruise lift-to-drag ratio by 7.1% and 6.4%, respectively. The entire design process was completed in less than an hour on a personal computer, highlighting the high efficiency and scalability of the EvoGD approach.

关键词： evolutionary computation Artificial neural network Generative learning Supercritical airfoil design

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Business failure prediction models with high and stable predictive power over time using genetic programming

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OPERATIONAL RESEARCH 2024年第3期24卷 52页

作者： Beade, Angel Rodriguez, Manuel Santos, Jose Univ A Coruna Business Dept Campus Elvina S-N La Coruna 15071 Spain UIE Catedra AECA Abanca Abanca Spain Univ A Coruna CITIC Ctr Informat & Commun Technol Res Dept Comp Sci & Informat Technol Campus Elvina S-N La Coruna 15071 Spain

This study focuses on the deterioration of the predictive power and the analysis of the predictive stability of business failure prediction models, an aspect not sufficiently analysed in previous research. Insolvency prediction is considered with three temporal horizons (1 year, 3 years and 5 years prior to failure). The Genetic Programming (GP) tool has been used to achieve prediction models with high performance and stability over time, considering a long post-learning period in the stability analysis. In addition, novel scenarios representative of actual model use are proposed and considered, as well as metrics to assess the deterioration of the models' predictive power. The optimised GP prediction models (in the three temporal horizons) present a higher performance with respect to external references and, more importantly in relation to the objective of our study, the selected GP models substantially improve on the stability reported in previous studies, meeting the pursued requirements of degree of deterioration (less than 5%) and stability (Pearson's coefficient of variation less than 5%). Thus, the predictions of the GP models after the learning are very stable (period 2008-2019), to a certain extent immune, with respect to their environment, responding adequately in both procyclical and countercyclical modes, all of which is particularly relevant as this period includes a strong recession and a strong recovery. This should help to increase the reliability of business failure prediction models. Moreover, the relevance of including variables other than the usual financial ratios as predictors of failure is confirmed.

关键词： Business failure Financial ratios Prediction stability evolutionary computation Genetic programming

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Optimized Weights for Heterogeneous Epidemic Spreading Networks: A Constrained Cooperative Coevolution Strategy

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IEEE TRANSACTIONS ON computationAL SOCIAL SYSTEMS 2023年第3期11卷 3911-3919页

作者： Feng, Yun Wang, Yaonan Wang, Bing-Chuan Ding, Li Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Hunan Univ Natl Engn Res Ctr Robot Visual Percept & Control T Changsha 410082 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China

The wide spreading of COVID-19 all over the world raises numerous focus on the epidemic containment problem. Different from the traditional epidemic control strategies that focus on quarantine and vaccination, we seek to control the epidemic from a network science perspective, i.e., by adjusting the weights of the epidemic spreading networks. Moreover, considering the limitations on the available resources, the dynamic constrained optimization problem of weights' adaptation for heterogeneous epidemic spreading networks is investigated. Due to the powerful ability of searching for global optimum, evolutionary algorithms (EAs) are used as optimizers. One major difficulty is that the dimension of the problem is increasing exponentially with the network size and most existing EAs cannot achieve satisfiable performance on large-scale optimization problems. To address this issue, a novel constrained cooperative coevolution ( C-3 ) strategy, which can separate the original large-scale problem into different subcomponents, is used to achieve the tradeoff between the constraint and objective function.

关键词： Constrained optimization epidemic spreading evolutionary computation weights' adaptation

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Mining High Utility Itemset with Hybrid Ant Colony Optimization Algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第12期15卷 628-637页

作者： Mohan, Keerthi Anitha, J. RV Inst Technol & Management Dept Comp Sci & Engn Bangalore India

significant area of study within data mining is high-utility itemset mining (HUIM). The exponential problem of broad search space usually comes up while using traditional HUIM algorithms when the database size or the number of unique objects is huge. evolutionary computation (EC) -based algorithms have been presented as an alternate and efficient method to address HUIM problems since they can quickly produce a set of approximately optimum solutions. In transactional databases, finding entire high-utility itemset (HUIs) still need a lot of time using EC-based methods. In order to deal with this issue, we propose a hybrid Ant colony optimization-based HUIM algorithm. Genetic operators' crossover is applied to the generated solution by the ant in the Ant Colony optimization algorithm. In this study, a single-point crossover is employed wherein, the crossover point is selected randomly and a mutation operator is applied by changing one or many random bits in a string. This technique requires less time to mine the same number of HUIs than state-ofthe-art EC-based HUIM algorithms.

关键词： -Utility mining high utility itemset ant colony optimization genetic algorithm evolutionary computation

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TN-RSI: Trend-normalized RSI Indicator for Stock Trading Systems with evolutionary computation

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Procedia Computer Science 2014年 36卷 240-245页

作者： Ugur Sahin A. Murat Ozbayoglu TOBB University of Economics and Technology Department of Computer Engineering Ankara 06560 Turkey

RSI is a commonly used indicator preferred by stock traders. However, even though it works well when the market is trendless, during bull or bear market conditions (when there is a clear trend) its performance degrades. In this study, we developed a trading model using a modified RSI using trend-removed stock data. The model has several parameters including, the trend detection period, RSI buy-sell trigger levels and periods. These parameters are optimized using genetic algorithms; then the trading performance is compared against B&H and standard RSI indicator usage. 9 different ETFs are selected for evaluating trading performance. The results indicate there is a performance improvement both in profit and success rates using this new model. As future work, other indicators might be modelled in a similar fashion in order to see if it is possible to find one indicator that can work under any market condition.

关键词： stock market forecasting RSI genetic algorithms stock trading evolutionary computation trend detection

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