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

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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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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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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Robust nonlinear control of permanent magnet synchronous motor drives: An evolutionary algorithm optimized passivity-based control approach with a high-order sliding mode observer

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 145卷

作者： Belkhier, Youcef Fredj, Siham Rashid, Haroon Benbouzid, Mohamed Univ Brest UMR CNRS 6027 F-29238 Brest France Univ Bejaia Fac Technol Lab Technol Ind & Informat LTII Bejaia 06000 Algeria

Permanent Magnet Synchronous Machines (PMSMs) have revolutionized motor design by replacing traditional components like rotor windings, brushes, and sliding contacts with permanent magnets. This innovation has significantly improved operational efficiency and reduced maintenance needs. However, controlling PMSMs remains challenging due to the changing dynamics of the machine over time and its sensitivity to different environmental conditions. To tackle these challenges, this study presents a novel nonlinear control approach called passivity-based control (PBC). Unlike conventional methods, PBC manages both the electrical and mechanical dynamics of the system, focusing on energy flow and dissipation to maintain stability. To make the control more robust, the approach combines a nonlinear observer and a high-order sliding mode controller (HSMC), which enhance the system's ability to handle disturbances and parameter changes. Additionally, the study uses Genetic Algorithm (GA) optimization to fine-tune the parameters of the PBC, observer, and HSMC. This optimization improves the motor's tracking accuracy and robustness against external disruptions. The result is a control framework that preserves the natural dynamics of PMSMs while improving their stability and performance. Experimental validation using the platform for real-time simulation (OPAL-RT) and real world on a PMSM using dSPACE DS1202 board demonstrates that this method outperforms existing techniques under a variety of operating conditions, highlighting its effectiveness and reliability.

关键词： evolutionary algorithms Nonlinear control Passivity-based control Nonlinear observer Permanent magnet synchronous machines

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Critical vector based evolutionary algorithm for large-scale multi-objective optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-27页

作者： Zhu, Shuwei Wang, Wenping Fang, Wei Cui, Meiji Jiangnan Univ Sch Artificial Intelligence & Comp 1800 Lihu Rd Wuxi 214122 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Intelligent Mfg 200 Xiaolingwei St Nanjing 210094 Peoples R China

In this work, we propose a method for solving large-scale multi-objective problems based on problem transformation strategy. The key point of this method lies in how to construct the search subspace. First, the algorithm obtains a set of direction vectors in the decision space, which are combined in pairs to construct a set of subspaces. To obtain direction vectors with a uniform distribution as much as possible, we introduce the opposition-based learning strategy. Then, based on these subspaces, the original high-dimensional problem is transformed into a relatively lower-dimensional problem. A multi-objective evolutionary algorithm is used to quickly obtain a set of quasi-optimal solutions for the transformed lower-dimensional problem, and this set of solutions is further optimized in the original high-dimensional decision space. To validate its performance, the proposed algorithm is compared with six state-of-the-art large-scale multi-objective algorithms on various benchmark test problems, including one practical application. The experimental results demonstrate that the proposed algorithm shows competitive performance for dealing with large-scale multi-objective optimization problems.

关键词： evolutionary algorithms Large-scale optimization Multi-objective optimization Problem transformation

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Surrogate-assisted evolutionary neural architecture search based smart-block discovery

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 277卷

作者： Onzo, Bernard-marie Xue, Yu Neri, Ferrante Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Peoples R China Univ Surrey Sch Comp Sci & Elect Engn NICE Res Grp Guildford GU2 7XH England

Neural architecture search (NAS) has emerged as a powerful method for automating neural network design, yet its high computational cost remains a significant challenge. This paper introduces hybrid training-less neural architecture search (HYTES-NAS), a novel hybrid NAS framework that integrates evolutionary computation with a training-free evaluation strategy, significantly reducing computational demands while maintaining high search efficiency. Unlike conventional NAS methods that rely on full model training, HYTES-NAS leverages a surrogate-assisted scoring mechanism to assess candidate architectures efficiently. Additionally, a smart-block discovery strategy and particle swarm optimisation are employed to refine the search space and accelerate convergence. Experimental results on multiple NAS benchmarks demonstrate that HYTES-NAS achieves superior performance with significantly lower computational cost compared to state-of-the-art NAS methods. This work provides a promising and scalable solution for efficient NAS, making high-performance architecture search more accessible for real-world applications.

关键词： Neural architecture search Training-free evolutionary algorithms Surrogate models

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Enhancing transparency of omics data analysis with the evolutionary Multi-Test Tree and Relative Expression

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 276卷

作者： Czajkowski, Marcin Jurczuk, Krzysztof Kretowski, Marek Bialystok Tech Univ Fac Comp Sci Wiejska 45A PL-15351 Bialystok Poland

This paper presents a novel classification algorithm for multi-omics data, called evolutionary Multi-Test Tree with Relative Expression (EMTTree+RX). The innovation lies in the model's design, which integrates multi-test decision nodes with Relative Expression Analysis (RXA). Each decision node combines traditional univariate tests and top-scoring pair (TSP) comparisons, allowing the algorithm to capture complex relationships between features without relying solely on absolute values. This approach enables the proposed method to detect subtle patterns across various omics layers while maintaining a high level of interpretability, a feature crucial for clinical and bioinformatics applications. The tree structure is induced through evolutionary algorithms (EA), optimizing both the global architecture and local multi-test nodes to balance classification accuracy, test diversity, and feature cost. Applied to large-scale multi-omics datasets, where conventional decision tree methods often struggle with underfitting or overfitting, the proposed method consistently outperforms traditional models in terms of accuracy and transparency. This makes it a valuable tool for precision medicine and multi-modal data integration.

关键词： Multi-omics Decision trees Relative Expression Analysis evolutionary algorithms Top-scoring pair

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Dental X-Ray image enhancement using a novel evolutionary optimization algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 142卷

作者： Yildirim, Brahim Bozkurt, Mustafa Hakan Kahraman, Hamdi Tolga Aras, Sefa Kron Teknol Akdeniz Mah TR-35210 Izmir Turkiye Karadeniz Tech Univ Technol Fac Software Engn TR-61080 Trabzon Turkiye Trabzon Teknokent Maveria Technol TR-61081 Trabzon Turkiye Karadeniz Tech Univ Artificial Intelligence & Data Engn Fac Engn TR-61080 Trabzon Turkiye

As an optimization problem, the main challenges in the enhancement of dental X-ray images are to perform the tasks of edge detection, noise removal and brightness adjustment in a precise and efficient manner. To overcome these challenges, designing an optimization algorithm that exhibits exploit-exploit behavior in accordance with the geometric structure of the search space of dental images is an important challenge that has not yet been realized. Shortcomings in this area lead to local solution traps and early convergence problems in image enhancement algorithms. This paper introduces the dFDB-LSHADE (dynamic fitness-distance balance-based achievement-history-based adaptive differential evolution with linear population size reduction) algorithm, which is designed for the enhancement of dental X-ray images according to the requirements of the search space of this problem and dynamically changes its exploitation-exploration capabilities. The proposed method is tested on a dataset of 120 periapical images, the most extensive experimental study in the literature. A total of 60 competing algorithms, 53 heuristics and 7 deterministic-based algorithms, were used in the experiments. In the study on dental images, the proposed algorithm has a better Friedman score than all competitors. According to the statistical analysis results obtained from the Wilcoxon pairwise test, the proposed dFDB-LSHADE was able to find better solutions for 23.3% of the images compared to its strongest competitor on the dental image set.

关键词： Dental x-ray image processing Meta-heuristic image improvement Dynamic fitness distance balance Linear population size reduction optimization Artificial intelligence in dentistry evolutionary algorithms

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