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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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Gaussian process regression for evolutionary dynamic multiobjective optimization in complex environments

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SWARM AND evolutionary COMPUTATION 2025年 94卷

作者： Deng, Youpeng Zheng, Yan Meng, Zhaopeng Gao, Haobo Hua, Yueyang Jin, Qiangguo Cao, Leilei Tianjin Univ Coll Intelligence & Comp 135 Yaguan Rd Tianjin 300350 Peoples R China China Acad Launch Vehicle Technol 9200 Mailbox 1 Sub Box Beijing 100076 Peoples R China Northwestern Polytech Univ Sch Software 127 West Youyi Rd Xian 710072 Shaanxi Peoples R China Zhejiang Univ Innovat Ctr Yangtze River Delta 828 Zhongxing Rd Jiaxing 314100 Zhejiang Peoples R China

Multiobjective evolutionary algorithms (MOEAs) face significant challenges when addressing dynamic multiobjective optimization problems, particularly those with frequent changes. The complexity of dynamic environments makes it difficult for MOEAs to accurately approximate the true Pareto-optimal solutions before subsequent changes occur. Typically, historical approximations of Pareto-optimal solutions are utilized to predict solutions in future environments. However, existing predictors often overlook the nondeterministic nature of historical solutions, potentially compromising prediction accuracy. In this paper, we propose a novel predictor based on Gaussian Process Regression (GPR) for evolutionary dynamic multiobjective optimization. Unlike traditional deterministic predictors, our approach aims to provide a probability distribution of predicted results, thereby addressing the inherent nondeterminism of historical solutions. We employ GPR to model relationships among historical solutions across different time steps. Within the framework of the classical MOEA, MOEA/D, we introduce a new method MOEA/D-GPR for evolutionary Dynamic Multiobjective Optimization (EDMO). Experimental results demonstrate that our method achieves state-of-the-art performance.

关键词： Multiobjective optimization Dynamic environments evolutionary algorithms Nondeterminism Gaussian process regression

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evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 1001-1018页

作者： Ruan, Gan Minku, Leandro L. Xu, Zhao Yao, Xin Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT England NEC Labs Europe D-69115 Heidelberg Germany Lingnan Univ Sch Data Sci Hong Kong Peoples R China

In Open Radio Access Network (O-RAN), intelligent techniques are urged to achieve the automation of the computing resource allocation, so as to save computing resources and increase their utilization rate, as well as decrease the network delay. However, the existing formulation of this problem as an optimization problem defines the capacity utility of resource in an inappropriate way and it tends to cause much delay. Moreover, the only algorithm proposed to solve this problem is a greedy search algorithm, which is not ideal as it could get stuck into local optima. To overcome these issues, a new formulation that better describes the problem is proposed. In addition, an evolutionary algorithm (EA) is designed to find a resource allocation scheme to proactively and dynamically deploy the computing resource for processing upcoming traffic data. A multivariate long short-term memory model is used in the proposed EA to predict future traffic data for the production of deployment scheme. As a global search approach, the EA is less likely to get stuck in local optima than greed search, leading to better solutions. Experimental studies carried out on real-world datasets and artificially generated datasets with different scenarios and properties have demonstrated the significant superiority of our proposed EA over a baseline greedy algorithm under all parameter settings. Moreover, experimental studies with all afore-mentioned datasets are performed to compare the proposed EA and two variants under different parameter settings, to demonstrate the impact of different algorithm choices.

关键词： Resource management Open RAN Optimization Clustering algorithms Linear programming Greedy algorithms Delays Costs 5G mobile communication Search problems Dynamic optimization evolutionary algorithms open radio access network (O-RAN) resource allocation

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Advancements in Optimization: Critical Analysis of evolutionary, Swarm, and Behavior-Based algorithms

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algorithms 2024年第9期17卷 416页

作者： Rashed, Noor A. Ali, Yossra H. Rashid, Tarik A. Univ Technol Baghdad Comp Sci Dept Baghdad 10081 Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil 44001 Iraq

The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional methods to innovative metaheuristic techniques. The methods used for analysis include bibliometric analysis, keyword analysis, and content analysis, focusing on studies from the period 2000-2023. Databases such as IEEE Xplore, SpringerLink, and ScienceDirect were extensively utilized. Our analysis reveals that while traditional algorithms like evolutionary optimization (EO) and particle swarm optimization (PSO) remain popular, newer methods like the fitness-dependent optimizer (FDO) and learner performance-based behavior (LPBB) are gaining attraction due to their adaptability and efficiency. The main conclusion emphasizes the importance of algorithmic diversity, benchmarking standards, and performance evaluation metrics, highlighting future research paths including the exploration of hybrid algorithms, use of domain-specific knowledge, and addressing scalability issues in multi-objective optimization.

关键词： single-objective optimization metaheuristic algorithms multi-objective optimization optimization algorithms evolutionary algorithms scheduling algorithmic advancements

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Parking Vehicle-Assisted Task Offloading in Edge Computing: A dynamic multi-objective evolutionary algorithm with multi-strategy fusion response☆

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SWARM AND evolutionary COMPUTATION 2025年 94卷

作者： Zhou, Yingbo Chai, Zheng-Yi Li, Ya-Lun Li, Jun-Jie Tiangong Univ Sch Comp Sci & Technol Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Quanzhou Vocat & Tech Univ Quanzhou 362268 Fujian Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

Vehicle-edge computing, as a promising paradigm, is employed to support applications that require low latency and high computational capability. In this study, we consider the idle resources of the surrounding parked vehicles (PVs) and roadside units (RSUs) as service providers to enhance the performance of User Equipment (UE). We propose a joint offloading architecture that uses parked vehicles. Additionally, owing to the dynamic and uncertain nature of the environment, we model computation offloading as a dynamic multi-objective optimization problem to simultaneously optimize the latency and energy consumption of UE applications. In this study, we propose a dynamic multi-objective evolutionary algorithm with a multi-strategy fusion response (DMOEA/D-MSFR). Specifically, we introduce a population center positioning strategy and a learnable prediction mechanism using Long Short-Term Memory (LSTM) in DMOEA-MSFR, which divides the prediction optimization process into two stages and exhibits a rapid response to environmental changes. In the static optimization phase, an adaptive weight vector adjustment strategy is employed, which significantly aids in the distribution and diversity of the solutions. Comprehensive experiments demonstrate that our proposed framework balances the trade-off between latency and energy consumption, and the convergence, feasibility, and diversity of the non-dominated solutions obtained.

关键词： Vehicle edge computing Dynamic multi-objective optimization evolutionary algorithms Computational offloading Vehicle collaboration

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SEAMS: A surrogate-assisted evolutionary algorithm with metric-based dynamic strategy for expensive multi-objective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 265卷

作者： Liu, Haitao Wang, Chia-Hung Fujian Univ Technol Coll Comp Sci & Math 69 Xuefu South Rd Fuzhou City 350118 Fujian Province Peoples R China Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China

In real-world scenarios where resources for evaluating expensive optimization problems are limited and the reliability of trained models is hard to assess, the quality of the non-dominated front formed by algorithms tends to below. This paper proposes a metric-based surrogate-assisted evolutionary algorithm for multi-objective expensive optimization, incorporating a novel model management strategy that integrates a regeneration mechanism. This approach aims to achieve a well-balanced convergence and diversity, facilitating the attainment of high-quality non-dominated fronts to address expensive multi-objective optimization problems. The model management strategy, based on metrics, comprehensively evaluates the reliability of the classification model and selects appropriate strategies for offspring selection. Moreover, through significance analysis of the population, the regeneration mechanism identifies high-quality dimensions for regenerating offspring. The algorithm maximizes the utilization of the classification model to guide the generation and selection of offspring in the population. Experiments on DTLZ, MaF, WFG, and the high-dimensional portfolio optimization problem demonstrate that the proposed algorithm outperforms nine state-of-the-art surrogate-assisted evolutionary algorithms, highlighting its superior performance across various scenarios.

关键词： evolutionary algorithms Surrogate-assisted optimization Expensive multi-objective optimization Classification Model management Front modeling

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