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A Power Control Algorithm for V2V Communication Networks Based on dynamic multi-objective optimization

IEEE ACCESS 2025年 13卷 54823-54835页

作者： Ma, Mengyu Gu, Zihao Wang, Chao Li, Zuxing Hu, Jianyao Liu, Fuqiang Tongji Univ Dept Informat & Commun Engn Shanghai 201804 Peoples R China Minist Ind & Informat Technol Elect Res Inst 5 Guangzhou 510610 Peoples R China

Vehicular communication networks have the nature of high dynamics and consist of communication links with different or even conflicting transmission objectives and quality of service (QoS) requirements. Therefore, it is extremely challenging to make optimal transmission decisions in vehicular communication networks. In this paper, we study a sequential transmission design problem in a typical vehicle-to-vehicle (V2V) communication network and formulate the problem as a dynamic multi-objective optimization (DMO) problem with the aim to trade-off transmission objectives and guarantee QoS requirements through power control. We propose a prediction-based dynamic multi-objective optimization evolutionary algorithm (DMOEA) that facilitates the evolution of the solution population by predicting the centroid of the power allocation decision set in a new environment, so that transmission decisions can be made to adapt to the highly dynamic environment. Extensive simulation experiments demonstrate the effectiveness and advantages of the proposed algorithm.

关键词： V2V communication dynamic multi-objective optimization sequential transmission design V2V communication dynamic multi-objective optimization sequential transmission design

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Twin-population multiple knowledge-guided transfer prediction framework for evolutionary dynamic multi-objective optimization

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APPLIED SOFT COMPUTING 2025年 175卷

作者： Zhao, Shijie Zhang, Tianran Chen, Miao Zhang, Lei Liaoning Tech Univ Inst Intelligence Sci & Optimizat Fuxin 123000 Peoples R China Liaoning Tech Univ Inst Optimizat & Decis Analyt Fuxin 123000 Peoples R China

dynamic multi-objective evolutionary algorithms (DMOEAs) have been widely studied, and one of the main tasks is the need for algorithms to track Pareto optimal front (POF) under dynamic environmental changes. Existing methods integrate transfer learning (TL) techniques to predict the initial population for the new environment. However, the lack of transferred individual diversity and inaccurate moving directions lead to poor performance of DMOEAs. Therefore, this work proposes a Twin-population multiple Knowledge-guided Transfer prediction (TMKT) framework to form an initial population for the new environment. Three strategies, i.e., Twin Populations Guided prediction (TPG), SVM-based multi-knowledge prediction (SVM-M) and Kernel Subspace Alignment for Transfer prediction (KSA-T), are designed to mine and transfer positive historical knowledge for accurately predicting changing POFs. First, TPG is used to obtain new approximate individuals and provide potential directions of subsequent transfer, which splits the population into two twin populations based on upper and lower quartile points of the first objective and their angles. Subpopulations transmit information by different similarity methods to obtain their new positions. Secondly, to obtain solutions with better diversity and convergence, SVM-M trains a certain classifier that can discriminate between positive and negative solutions and predicts labels of noisy solutions based on useful knowledge from the first two environments. Third, KSA-T is proposed to further enhance the accuracy of the new population prediction. The kernel trick and second-order feature alignment are introduced in subspace alignment to develop a new TL technique called Kernel Subspace Alignment (KSA) for adaptively achieving homotypic distributions of the source domain and target domain. Solutions predicted by TPG as the target domain are employed to guide the evolution, and obtainedSVM-M positive solutions are transferred to the new envi

关键词： dynamic multi-objective optimization Twin-population multiple knowledge-guided transfer Kernel subspace alignment

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A Special Points and Neural Network-Based dynamic multi-objective optimization Algorithm

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IEEE ACCESS 2025年 13卷 24765-24792页

作者： Li, Sanyi Hou, Wenjie Liu, Peng Yue, Weichao Wang, Qian Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Peoples R China

This paper introduces a special points and neural network- based dynamic multi-objective optimization algorithm (SPNN-DMOA) for solving dynamic multi-objective optimization problems (DMOPs) with an irregularly changing pareto set. In the stage of population initialization, the algorithm employs a feedforward neural network (FNN) along with special points to generate an initial population. The FNN is trained with historical special points (knee point, boundary point, center point), and the current special points are generated by the FNN when an environmental change is detected. Then the decision variables are classified into convergence variables and diversity variables. The convergence variables of special points are locally searched to form a new population and the best individuals of this population are selected. Finally, a portion of the initial population is generated by conducting a local search on the diversity variables of best individuals, while the remaining portion is produced using random strategies. SPNN-DMOA only needs to maintain non-dominated solutions in proximity to special points, which reduces the computational complexity in the dynamic evolution process. The proposed algorithm has been compared with other state-of-the-art algorithms on a series of benchmark problems, demonstrating its superior performance in optimizing DMOPs.

关键词： Heuristic algorithms optimization Convergence Pareto optimization Classification algorithms Prediction algorithms Diversity reception Vectors Optical fibers Machine learning algorithms dynamic multi-objective optimization decision variable classification irregular environment neural network prediction special point

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Anew prediction strategy for dynamic multi-objective optimization using hybrid Fuzzy C-Means and support vector machine

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NEUROCOMPUTING 2025年 621卷

作者： Zhang, Tao Tao, Qing Yu, Linjun Yi, Haohao Chen, Jiawei Yangtze Univ Sch Informat & Math Jingzhou 434023 Peoples R China Southwest Univ Sch Math & Stat Chongqing 4007150 Peoples R China

A dynamic multi-objective optimization problem (DMOP) involves optimizing multiple conflicting objectives that change over time. It presents a significant challenge in rapidly adapting to evolving environments and tracking the Pareto Front. Recently, there has been increased interest in prediction-based methods due to their effectiveness and broad applicability. However, many predictive methods focus mainly on the correlation of solutions between successive generations, neglecting the overall connectedness, which leads to predicted solutions deviating from the evolutionary direction. This paper proposes a novel dynamic multi-objective optimization prediction method, called FCM-SVM-DMOEA. This method utilizes the Fuzzy C-Means (FCM) algorithm to cluster historical Pareto sets. The outcomes of Fuzzy C-Means are divided into high-quality and low-quality sets employing the Fast Non-Dominated Sorting method based on the Center Point of the Cluster. Subsequently, a Support Vector Machine (SVM) classifier is adopted to train these two sets. When environmental changes occur, the initial population consists of both variations of high-quality set and random solutions identified by the SVM classifier. Experimental results from DF suite benchmark test problems, in comparison with selected state-of-the-art algorithms, indicate that the Pareto set traced by the proposed method exhibits superior diversity and distribution, markedly improving the performance of dynamic multi-objective optimization algorithms.

关键词： dynamic multi-objective optimization Fuzzy C-means Support vector machine Prediction-based reaction

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A Discriminative Prediction Strategy Based on multi-View Knowledge Transfer for dynamic multi-objective optimization

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PROCESSES 2025年第3期13卷 744-744页

作者： Xu, Hua Zhang, Chenjie Huang, Lingxiang Tao, Juntai Zheng, Jianlu Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China

dynamic multi-objective optimization problems (DMOPs) are widely encountered in engineering optimization processes, characterized by conflicting objectives that change over time. Evolutionary transfer optimization (ETO) has recently emerged as a promising optimization paradigm for addressing DMOPs. ETO-based dynamic multi-objective evolutionary algorithms (DMOEAs) usually adopt discriminative prediction strategies to select high-quality initial solutions in new environments by transferring knowledge from the source domain, thereby accelerating the evolutionary process. However, DMOPs often change in both decision and objective spaces. Existing methods rely on a single view from the source domain for knowledge transfer, which can overlook crucial knowledge from other views, potentially affecting the selection of high-quality initial solutions and limiting optimization performance. To address this, we propose a discriminative prediction strategy based on multi-view knowledge transfer for DMOEA, called MKT-DMOEA. Specifically, we construct discriminative predictors from the view of the decision space and objective space in the source domain. Each discriminative predictor effectively reduces the differences between environments under the current view through a transfer strategy based on geometric feature transformation. Meanwhile, these predictors make full use of the transferred knowledge to achieve accurate discriminative predictions under the current view. Finally, we integrate the discriminative prediction results from multiple views to select high-quality initial solutions. Experimental results demonstrate that our proposed algorithm outperforms four other state-of-the-art DMOEAs in terms of both diversity and convergence on the well-known CEC 2018 dynamic multi-objective optimization benchmark suite DF.

关键词： dynamic multi-objective optimization evolutionary transfer optimization evolutionary algorithm transfer learning prediction

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A dynamic multi-objective optimization based on knowledge prediction and density clustering strategy

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APPLIED SOFT COMPUTING 2025年 175卷

作者： Wang, Yong Wang, Shengao Li, Kuichao Wang, Gai-Ge Ocean Univ China Sch Comp Sci & Technol Qingdao 266100 Peoples R China

dynamic multi-objective evolutionary algorithms (DMOEAs) that extract historical knowledge from the past environment to predict new solutions are known to be effective for solving dynamic multi-objective optimization problems (DMOPs). However, most of the existing methods simply reuse historical solutions without further extracting the knowledge between different historical environment solutions, which may make the algorithm ignore some important historical knowledge and limit its performance. In this paper, we propose a knowledge prediction strategy and a density clustering strategy for DMOEA, called KPDCS-DMOEA, which aim to extract historical knowledge from the past environment to build a more accurate prediction model. Firstly, the trend of change in the initial environment is obtained by predicting previous environmental changes through linear prediction methods based on knee point clusters. Secondly, a strategy was proposed to pair the solutions between adjacent environments and construct each dimensional motion vector as historical knowledge. The training set is constructed according to the motion step of the motion vector and the motion direction of each dimension, and the neural network is trained to predict the initial population in the new environment. Finally, a guided evolution strategy based on a density clustering algorithm is developed to speed up population convergence and ensure that the population is well distributed. KPDCS-DMOEA is compared with several state-of-the-art DMOEAs. Experimental results show that the performance of KPDCS-DMOEA is better than the selected comparison algorithms.

关键词： dynamic multi-objective optimization Prediction Neural network Cluster Evolutionary algorithm

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Adaptive multi-region prediction strategy for dynamic multi-objective optimization

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APPLIED SOFT COMPUTING 2025年 176卷

作者： Zhang, Tao Yu, Linjun Yu, Huiwen Yangtze Univ Sch Informat & Math Jingzhou 434023 Peoples R China MIT 77 Massachusetts Ave Cambridge MA 02139 USA

This paper proposes a novel adaptive multi-region prediction strategy for dynamic multi-objective optimization problems (DMOPs), which aims to efficiently generate diverse populations in response to environmental changes and facilitate the exploration of the new Pareto front. The strategy consists of two main phases: predictive population initialization and elite-guided resampling. In the predictive population initialization phase, the strategy integrates global exploration and local exploitation. Global exploration divides the population into N subregions based on population distribution characteristics. For each subregion, the historical information of its center point is used to predict its new position in the next environment, and then a Gaussian mixture model (GMM) is used to sample new individuals based on the position information of all new center points. Local exploitation employs the K-Medoids method to cluster historical Pareto fronts and selects individuals corresponding to the medoids in the decision space as representative individuals. These representative individuals are then used to predict their new locations, followed by Gaussian sampling to generate individuals. The initial predicted population is formed by combining the individuals from global exploration, local exploitation, and randomly generated individuals. In the elite-guided resampling phase, the initial predicted population is evaluated, and top-ranked elite individuals are selected. These elites guide the generation of the final population through Gaussian sampling and Latin Hypercube Sampling (LHS), enhancing solution quality and diversity. The proposed strategy is validated on 14 benchmark problems using MIGD, MHV, R(IGD), and DMIGD metrics. Results demonstrate its better comprehensive performance under varying environmental change intensities (mild, moderate, and severe) compared to existing approaches. Furthermore, its application to a real-world PID controller tuning problem highlight

关键词： dynamic multi-objective optimization Elite-guided resampling Gaussian mixture model

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A dynamic multi-objective optimization evolutionary algorithm based on classification of environmental change intensity and collaborative prediction strategy

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-52页

作者： Wang, Yu Ma, Yongjie Li, Quanxiu Zhao, Yan Northwest Normal Univ Coll Phys & Elect Engn Lanzhou 730070 Peoples R China

The dynamic multi-objective optimization evolutionary algorithm (DMOEA) has garnered widespread attention due to its superiority in solving dynamic multi-objective optimization problems (DMOPs). Existing DMOEAs do not judge the intensity of environmental changes after they have been detected, which may lead to incorrect evolutionary directions of the population. To address this issue, this study proposes a DMOEA based on the classification of environmental change intensity and collaborative prediction strategy. Firstly, the algorithm optimizes the static optimization process, thereby determining the relative position of individuals in the objective space and enhancing the accuracy of environmental change detection. Upon detecting an environmental change, the algorithm proposes a method based on mutual information to further classify the intensity of the environmental change, and guides the particle swarm to adopt different velocity update methods for evolution based on the classification results. Secondly, a collaborative prediction strategy is proposed to ensure that the predicted population is closer to the Pareto optimal solution Set (PS) in the new environment. Lastly, a dual individual screening strategy is employed to select superior individuals from both the predicted population and the population before the environmental change to form the initial population in the new environment. Comparative experiments with advanced DMOEAs on 20 different types of test functions demonstrate the superiority of the proposed algorithm in solving complex DMOPs.

关键词： dynamic multi-objective optimization Classification of environmental change intensity Collaborative prediction strategy Dual individual screening strategy Particle swarm prediction

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A Proactive Resource Allocation Algorithm for UAV-Assisted V2X Communication Based on dynamic multi-objective optimization

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IEEE COMMUNICATIONS LETTERS 2024年第12期28卷 2814-2818页

作者： Ma, Mengyu Wang, Chao Li, Zuxing Liu, Fuqiang Tongji Univ Dept Informat & Commun Engn Shanghai 201804 Peoples R China

This letter addresses a proactive resource allocation problem in an unmanned aerial vehicles (UAV)-assisted vehicle-to-everything (V2X) communication network. The problem, which can be formulated from the perspective of dynamic multi-objective optimization (DMO), focuses on the decisions of power control, rate selection, and channel allocation with the aim to balance the decision objectives of heterogeneous V2X links while ensuring the network's quality of service (QoS). We propose a novel algorithm that enables quick decision-making in a dynamic environment, by predicting the future channel state information (CSI) and searching for the solution set of the problem before the environment changes. The effectiveness and advantages of the proposed method are demonstrated by simulation experiments.

关键词： Heuristic algorithms Vehicle-to-everything Fading channels Vehicle dynamics Resource management Autonomous aerial vehicles Reliability optimization Communication networks Signal to noise ratio V2X communication dynamic multi-objective optimization proactive resource allocation

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Integrating machine learning with dynamic multi-objective optimization for real-time decision-making

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INFORMATION SCIENCES 2025年 690卷

作者： Sarkar, Puja Khanapuri, Vivekanand B. Tiwari, Manoj Kumar Indian Inst Management Mumbai Mumbai India Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 West Bengal India

Real-time decision-making in dynamic multi-objective optimization problems (DMOPs) is challenging due to constantly changing objectives and constraints. This paper integrates machine learning with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve DMOPs and make real-time decisions. Learning-based methods have gained popularity for predicting solutions in new environments and capturing changing patterns in optimal solutions. However, existing approaches often struggle with training difficulty and reduced prediction accuracy due to irrelevant or redundant variables. Therefore, we introduce a new interdependent prediction (IDP) technique to identify correlations between variables and prediction targets and select significant variables for a predictive model. In this way, a better initial population is predicted. The IDP strategy is integrated within the dynamic NSGA-II, introducing a new algorithm called IDP-DNSGA-II. This integration facilitates rapid convergence, finding optimal or near-optimal solutions. The proposed method is evaluated against standard benchmarks, demonstrating superior performance in convergence speed and solution diversity with the changes in the problem environment. The IDP-DNSGA-II is validated through real-world optimization challenges in sustainable automobile production distribution in order-to-delivery systems to enhance environmental sustainability and operational efficiency. This study identifies the minimum frequency of change required in real- world problems to adequately track the optimal decision in real-time.

关键词： Machine learning Correlation dynamic multi-objective optimization Evolutionary computation

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