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Objective transformation-based and niche-based many-objective evolutionary algorithm with a two-step coordination mechanism

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 141卷

作者： Luo, Jiale Gu, Qinghua Li, Xuexian Chen, Lu Xian Univ Architecture & Technol Sch Management Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Sch Resources Engn Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Xian Key Lab Intelligent Ind Percept Calculat & De Xian 710055 Peoples R China

evolutionary algorithms have emerged as powerful tools for optimization. However, striking a balance between convergence and diversity in many-objective optimization remains a significant challenge. To address this gap, we propose TSEA-OTN, an objective transformation-based and niche-based many-objective evolutionary algorithm with a two-step coordination mechanism. Uniquely, TSEA-OTN operates without relying on relaxed Pareto dominance, reference vectors, or additional indicators. Instead, it utilizes prior knowledge about the curvature of the PF (Pareto optimal front) to transform the objectives of the population and establish niches. Additionally, a niche-assisted density estimation method is designed to measure the distribution of individual. The environmental selection process incorporates a two-step mechanism: in the former step, the niche-assisted density evaluation method identifies crowded individuals to prioritize diversity;in the latter step, the Euclidean distance among transformed individuals and convergence evaluation criteria are used to eliminate individuals within the same niche for promoting convergence. Finally, TSEA-OTN is evaluated against six state-of-the-art algorithms on DTLZ (Deb-Thiele-Laumanns-Zitzler), MaF (Many-objective function), WGF (Walking Fish Group) benchmark suites, as well as an engineering case study. Experimental results demonstrate the competitive performance of TSEA-OTN in solving many-objective optimization problems. This research not only advances the field of evolutionary computation but also provides novel solutions for real-world optimization.

关键词： evolutionary algorithms Many-objective optimization Objective transformation Niche

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An adaptive matrix-based evolutionary computation framework for EEG feature selection

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MEMETIC COMPUTING 2025年第1期17卷 1-19页

作者： Duan, Danting Sun, Bing Yang, Qiang Ye, Long Zhang, Qin Zhang, Jun Commun Univ China Key Lab Media Audio & Video Minist Educ Beijing Peoples R China Nankai Univ Coll Artif Intelligence Tianjin Peoples R China Nanjing Univ Informat Sci & Technol Sch Artif Intelligence Nanjing Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan South Korea

Electroencephalogram (EEG) plays a significant role in emotion recognition because it contains abundant information. However, due to the highly correlated EEG channels, a lot of redundant EEG features exist, which not only potentially degrade the emotion recognition accuracy, but also bring high computational costs. To address this challenge, this paper proposes an adaptive matrix-based evolutionary computation framework (AMEC) to select as few informative EEG features as possible for effective emotion recognition. Unlike most existing EC algorithms that utilize vector-based operations, this framework leverages matrix-based operations to reduce feature redundancy and improve classification accuracy by dynamically adjusting the feature subset size according to the characteristics of the dataset. In such a way, the selection efficiency is largely improved. To verify the effectiveness and efficiency of this framework, the classical genetic algorithm, the typical particle swarm optimization algorithm, and the classical differential evolution algorithm, are respectively embedded into this framework for EEG feature selection, and then evaluated on three widely used public EEG datasets for emotion recognition. Compared with several state-of-the-art EEG feature selection algorithms, the devised framework is much more effective in terms of the classification accuracy and the computational efficiency. In addition, the experimental results further reveal that the selected feature subsets are very different for different genders. This indicates the demand of gender-sensitive EEG feature selection for emotion recognition.

关键词： EEG Feature selection Matrix-based evolutionary computation Emotion recognition evolutionary algorithms

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System identification for T1D via artificial intelligence algorithms: evolutionary or swarm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期85卷

作者： Flores-Gutierrez, Claudia Patricia Quiroz-Compean, Griselda Renteria-Vidales, Octavio Torres-Trevino, Luis Ruiz-Velazquez, Eduardo Femat, Ricardo Univ Politecn San Luis Potosi Acad Tecnol Informac Urbano Villalon 500 San Luis Potosi 78363 Slp Mexico Univ Autonoma Nuevo Leon Fac Ingn Mecan & Elect Ave Univ S-NCd Univ San Nicolas De Los Garza 66455 NL Mexico Ctr Invest Matemat AC Jalisco S-NApartado Postal 402 Guanajuato 36000 Gto Mexico CUCEI UDG Div Elect & Comp Blvd Marcelino Garcia Barragan 1421 Guadalajara 44430 Jal Mexico IPICyT Div Control & Sistemas Dinam Camino Presa San Jose 2055Lomas Secc 4a San Luis Potosi 78216 Slp Mexico

This work contributes on how a parameter optimization scheme tackles the system identification problem in type 1 diabetes (T1D) patients to derive a dynamical model with potential application on feedback control schemes for blood glucose regulation. That is, the contribution aim is to identify values of system (sensitive) parameters such that a patient-specific model is derived towards the future design of feedback control for assisting T1D therapy. To this end, a differential equation system is proposed to model the blood glucose dynamics in T1D. A challenge in control systems regards the system identification to capture suitable response from available measurements of the blood glucose levels. T1D is particularly interesting due to the glycemia inter-variability. This fact has been recently highlighted because of the continuous glucose monitoring has revealed the need of specificity at glucose dynamics model for every single patient. Hence, a class of artificial intelligence algorithms are performed towards the identification of a control system for the individual glucose metabolism. Here, three AI algorithms perform the system identification towards future implementation. Each one of the three AI algorithms comprises two parts: a physiological model and a parameter optimization scheme to capture the time response from a set of glucose data obtained from measurements. The explored AI algorithms for the parameter optimization are respectively approached via Genetic Algorithm, Particle swarm optimization algorithm, and Taguchi sliding based differential evolution algorithm. A set of patients allows us to explore experimentally the performance of the AI algorithms.

关键词： T1D blood glucose identification Patient-specific models evolutionary algorithms Swarm intelligence

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evolutionary optimization of spatially-distributed multi-sensors placement for indoor surveillance environments with security levels

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 166卷

作者： Moreno-Saavedra, Luis M. Costa, Vinicius G. Garrido-Saez, Adrian Jimenez-Fernandez, Silvia Portilla-Figueras, J. Antonio Salcedo-Sanz, Sancho Univ Alcala Dept Signal Proc & Commun Alcala De Henares 28805 Madrid Spain Fed Univ Rio Janeiro Syst & Comp Engn Program BR-21941617 Rio De Janeiro Brazil

The surveillance multi-sensor placement is an important optimization problem that consists of positioning several sensors of different types to maximize the coverage of a determined area while minimizing the cost of the deployment. In this work, we tackle a modified version of the problem, consisting of spatially distributed multi-sensor placement for indoor surveillance. Our approach is focused on security surveillance of sensible indoor spaces, such as military installations, where distinct security levels can be considered. We propose an evolutionary algorithm to solve the problem, in which a novel special encoding (integer encoding with binary conversion) and effective initialization have been defined to improve the performance and convergence of the proposed algorithm. We also consider the probability of detection for each surveillance point, which depends on the distance to the sensor at hand, to better model real-life scenarios. We have tested the proposed evolutionary approach in different instances of the problem, varying both size and difficulty and obtained excellent results regarding the cost of sensors' placement and convergence time of the algorithm.

关键词： Sensor placement problem Wireless network deployment evolutionary algorithms Indoor surveillance Security levels

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evolutionary algorithms for hyperparameter tuning on neural networks models 26

Evolutionary algorithms for hyperparameter tuning on neural ...

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26th European Modeling and Simulation Symposium, EMSS 2014

作者： Orive, David Sorrosal, Gorka Borges, Cruz E. Martin, Cristina Alonso-Vicario, Ainhoa Deusto Institute of Technology - DeustoTech Energy University of Deusto Avda. Universidades 24 Bilbao Spain

ISBN: (纸本)9788897999324

In this work we present a comparison of several Artificial Neural Networks weights initialization methods based on evolutionary algorithms. We have tested these methods on three datasets: KEEL regression problems, random synthetic dataset and a dataset of concentration of different chemical species from the Bioethanol To Olefins process. Results demonstrated that the tuning of neural networks initial weights improves significantly their performance compared to a random initialization. In addition, several crossover algorithms were tested to identify the best one for the present objective. In the post-hoc analysis there were found significant differences between the implemented crossover algorithms when the network has four or more inputs.

关键词： evolutionary algorithms

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Identification of Ferrite Core Inductors Parameters by evolutionary algorithms

Identification of Ferrite Core Inductors Parameters by Evolu...

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International Conference on Industrial Informatics

作者： Kateryna Stoyka Giulia Di Capua Antonio Della Cioppa Nicola Femia Giovanni Spagnuolo Department of Information Engineering Electrical Engineering and Applied Mathematics (DIEM) University of Salerno Fisciano (SA) 84084 ITALY

ISBN: (纸本)9781479966509

This paper discusses the identification of Ferrite Core (FC) power inductors parameters in the real operating conditions relevant to Switch-Mode Power Supplies starting from experimental measurements. A novel method for parameters identification is proposed, based on evolutionary algorithms (EAs) and on the analysis of inductors non-linear behavior. Two EAs, the Genetic Algorithm and the Differential Evolution, are investigated and compared. The results of the proposed method are experimentally validated by means of a buck converter evaluation board.

关键词： evolutionary algorithms Differential Evolution Genetic Algorithm Power Inductors Parameters Identification

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Handling objective preference and variable uncertainty in evolutionary multi-objective optimization

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SWARM AND evolutionary COMPUTATION 2025年 94卷

作者： Yadav, Deepanshu Ramu, Palaniappan Deb, Kalyanmoy Indian Inst Technol Madras Dept Engn Design Chennai 600036 India Michigan State Univ Dept Elect & Comp Engn E Lansing MI USA

evolutionary algorithms (EAs) are widely employed in multi-objective optimization (MOO) to find a well- distributed set of near-Pareto solutions. Among various types of practicalities that demand standard evolutionary multi-objective optimization (EMO) algorithms to be modified suitably, we propose here a framework for handling two important ones: (i) decision-making to choose one or more preferred Pareto regions, rather than finding the entire Pareto set, and (i) uncertainty in variables and parameters of the problem which is inevitable in any practical problem. While the first practicality will allow a focused set of preferred solutions to be found, the second practicality will enable finding robust yet high-performing non-dominated solutions. We propose and analyze four different approaches for finding preferred and robust solutions for handling both practicalities simultaneously. Our results on a number of two to 10-objective tests and engineering problems indicate the superiority of one specific approach. Fora comprehensive evaluation of new EMO algorithms for finding a preferred and robust solution set, we also propose anew performance metric by identifying and utilizing a number of desired properties of such trade-off solutions. The study is comprehensive and should encourage researchers to develop more competitive EMO algorithms for finding preferred and robust Pareto solutions.

关键词： evolutionary algorithms Pareto front Robust front Multi-criteria decision-making Preference-based EMO

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On Increasing Computational Efficiency of evolutionary algorithms Applied to Large Optimization Problems

On Increasing Computational Efficiency of Evolutionary Algor...

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IEEE Congress on evolutionary Computation

作者： Maciej Glowacki Janusz Orkisz Institute for Computational Civil Engineering Cracow University of Technology Cracow Poland

ISBN: (纸本)9781479974931

This paper presents new advances in development of dedicated evolutionary algorithms (EA) for large non-linear constrained optimization problems. The primary objective of our research is a significant increase of the computational efficiency of the standard EA. The EA are understood here as Genetic algorithms using decimal chromosomes, three standard operators: selection, crossover, and mutation, as well as additional new speed-up techniques. So far we have preliminarily proposed several general concepts, including smoothing and balancing, a'posteriori solution error analysis and related techniques, as well as an adaptive step-by-step mesh refinement. We discuss here the efficiency of chosen speed-up techniques using simple but demanding benchmark problems, including residual stress analysis in elastic-perfectly plastic bodies under cyclic loadings, and physically based smoothing of experimental data. Particularly, we consider a smoothing technique using average solution curvature, new criteria for selection based on global solution error, as well as a step-by-step mesh refinement combined with smoothing. Preliminary numerical results clearly indicate a possibility of significant acceleration of calculations, as well as practical application of the improved EA to the optimization problems considered.

关键词： evolutionary algorithms computation efficiency increase large non-linear constrained optimization problems

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Exploratory analysis and evolutionary computing coupled machine learning algorithms for modelling the wear characteristics of AZ31 alloy

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MATERIALS TODAY COMMUNICATIONS 2023年 37卷

作者： Mishra, Akshansh Jatti, Vijaykumar S. Sefene, Eyob Messele Politecn Milan Sch Ind & Informat Engn Milan Italy Symbiosis Inst Technol Dept Mech Engn Pune 412115 India Natl Taiwan Univ Sci & Technol Dept Mech Engn Taipei 10607 Taiwan

The wear resistance of magnesium alloys is one of its key technological properties that could limit their practical application. In accordance with ASTM G99-95a standard, this study used a pin-on-disc method to analyze the wear behavior of ascast AZ31 magnesium alloy under dry-sliding conditions. With a track radius of 37.5 mm, varied sliding velocities of 0.25, 0.5, 1, and 1.5 m/s, and normal loads of 40, 60, and 90 N were employed to quantify wear rate over a fixed sliding distance of 600 m. The surface morphology of the alloy's corroded surface was investigated using a SEM/EDS. The effectiveness of two evolutionary Computing integrated machine learning algorithms, Particle Swarm Optimization coupled Decision Tree (PSO-DT) and Particle Swarm Optimization coupled Gradient Boosting Regressor (PSO-GBR), is also compared in this study in predicting the particular wear rate of AZ31 magnesium alloy. The experimental observations of wear behavior at various sliding velocities and normal loads make up the dataset used in this study. The algorithms' prediction performance was assessed using the coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE). The results show that when it comes to foretelling the precise wear rate of AZ31 magnesium alloy, the PSO-GBR algorithm works better than the PSO-DT algorithm resulting in the R2 value of 0.99970. The PSO-GBR algorithm's successful integration of Particle Swarm Optimization and the gradient-boosting regressor model is responsible for this higher performance. The PSO-GBR algorithm improved accuracy and better captured nonlinear patterns in the data by improving the algorithm's parameters and capturing complex wear mechanisms.

关键词： evolutionary algorithms Magnesium Alloys Machine Learning Wear Rate

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Improving the Performance of evolutionary algorithms by Soft-Constraining their Sampling Capabilities

Improving the Performance of Evolutionary Algorithms by Soft...

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IEEE Congress on evolutionary Computation

作者： P. Caamano G. Varela R.J. Duro Integrated Group for Engineering Research Universidade da Coruna Spain

ISBN: (纸本)9781479974931

In this paper we argue that to produce good optimization performances, the exploration of the solution space does not need to be carried out in the unorderly fashion most evolutionary algorithms use. Other strategies that seek to minimize the cost involved in successive evaluation processes should be explored. This does not imply a fundamental change on how evolutionary algorithms work, but rather, it brings some structure onto how solution spaces are explored by contemplating decoding cost as one of the elements to be minimized when sampling. The traditional implementations of most evolutionary algorithms assume that any point in the solution space can be evaluated any time and at no cost. However, this is not always the case and often each step of the process only part of the solution space is available for evaluation giving rise to a class of problems we have called Constrained Sampling optimization problems over which evolutionary algorithms are quite inefficient. To address these problems we have proposed a modification of the general strategy of evolutionary algorithms to address these constraints efficiently. Here, we study the effects of this approach when applied to problems that are not constrained, thus modifying the way the solution space is explored. This study is carried out to determine how these modification impact the performance of a set of popular evolutionary algorithms over a representative set of benchmark functions corresponding to fitness landscapes with a variety of characteristics. We show that by restricting the sampling capabilities of most algorithms, the cost of the optimization procedure is reduced for most types of fitness landscapes without affecting their results.

关键词： Optimization evolutionary algorithms Constrained Sampling Problems Constrained Sampling evolutionary Algorithm

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