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dynamic multi-objective optimization method for production index of cement clinker firing process based on collaborative prediction strategy

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 151卷

作者： Liu, Gang Xie, Shangjian Hao, Xiaochen Yang, Mengke Yang, Xunian Xu, Xingxing Guo, Huan Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China

The cement clinker firing system is complex, with interdependent indicators, making it challenging to optimize decision-making. Furthermore, the traditional static single-objective or multi-objective optimization methods are inadequate in adapting to the dynamic changes of complex working conditions. To address these issues, this paper proposes a dynamic multi-objective optimization method for the production index of the cement clinker firing process. The way first establishes a data-driven dynamic multi-objective optimization model, the content of free calcium oxide (f-CaO) in clinker and the coal consumption of the sintering system as optimization objectives, numerous operational indicators as decision variables, considers dynamic factors and constraints during manufacturing. Then a dynamic multi-objective evolutionary algorithm based on a collaborative prediction strategy (CPS-DMOEA) is designed to solve the model. Experimental results for some benchmark test problems show a significant improvement in dynamic optimization performance with CPS-DMOEA. Furthermore, experiments using actual cement production data show that the proposed method outperforms traditional static multiobjective optimization methods.

关键词： Cement clinker firing process dynamic multi-objective optimization Data-driven model Collaborative prediction strategy

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multi-high-Q Terahertz biosensors based on a dynamic multi-objective optimization strategy

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OPTICS AND LASER TECHNOLOGY 2025年 183卷

作者： Li, Chun Chen, Haonan Zhu, Yifan Wang, Tengyu Teng, Yan Liang, Lanju Zhang, Yonggang Yao, Haiyun Huang, Zhengwei Jiang, Ling Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China Zaozhuang Univ Sch Optoelect Engn Zaozhuang 277160 Peoples R China Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232001 Anhui Peoples R China

Terahertz metamaterial sensors (TMS) playa key role in the highly sensitive detection of trace substances, especially in label-free, real-time, and in-situ measurements for disease, microbial, and pesticide residue diagnostics. To obtain excellent performance, the sensitivity of TMS is closely related to the resonance characteristics of the metamaterial, which depends on the optimal structural units. However, a desirable sensor often relies on experience and theory, and the design is gradually optimized through extensive manual calculations and simulations. Under the premise of ensuring the performance of terahertz devices, the use of algorithm to quickly generate customized device designs that meet specific needs has attracted widespread attention. Herein, we propose a strategy for designing multi-resonance TMS with high Q-factors and resonance depth based on dynamic multi-objective optimization (DMO). By introducing DMO, we designed high-Q TMS with 2-5 resonances simultaneously, saving about 122 min of design time compared with the traditional multi- objective strategy. To better demonstrate the performance of the sensor, we have characterized the 3-resonance TMS. The Q-factors of the optimized TMS can reach up to 301, 465, and 201, respectively. During the refractive index sensing process, the sensitivities of the three resonances (369, 376, and 387 GHz/RIU) have been achieved in the range of 0.9-1.7 THz. With the covered 2,4-D and thiabendazole analytes, the transmission spectra of the TMS demonstrate different "fingerprint peak"related characteristics. The proposed method can be used for dynamic multi-objective optimization challenges in various metamaterial device scenarios and provides a promising solution for designing multiple high-Q TMS.

关键词： THz metamaterial Biosensors dynamic multi-objective optimization

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A dynamic multi-objective optimization algorithm based on probability-driven prediction and correlation-guided individual transfer

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-47页

作者： Ge, Fangzhen Zhao, Xuan Chen, Debao Shen, Longfeng Liu, Huaiyu Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China Huaibei Normal Univ Anhui Engn Res Ctr Intelligent Comp & Applicat Cog Huaibei 235000 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China

The primary challenge in addressing dynamic multi-objective optimization problems (DMOPs) is the rapid tracking of optimal solutions. Although methods based on transfer learning have shown remarkable performance in tackling DMOPs, most existing methods overlook the potential relationships between individuals within the population and those from historical environments. Consequently, they fail to adequately exploit historical information. To this end, this study proposes a dynamic multi-objective optimization algorithm based on probability-driven prediction and correlation-guided individual transfer (PDP&CGIT), which consists of two strategies: probability-driven prediction (PDP) and correlation-guided individual transfer (CGIT). Specifically, the PDP strategy analyzes the distribution of population characteristics and constructs a discriminative predictor based on a probability-annotation matrix to classify high-quality solutions from numerous randomly generated solutions within the decision space. Moreover, from the perspective of individual evolution, the CGIT strategy analyzes the correlation between current elite individuals and those from the previous moment. It learns the dynamic change pattern of the individuals and transfers this pattern to new environments. This is to maintain the diversity and distribution of the population. By integrating the advantages of these two strategies, PDP&CGIT can efficiently respond to environmental changes. Extensive experiments were performed to compare the proposed PDP&CGIT with five state-of-the-art algorithms across the FDA, F, and DF test suites. The results demonstrated the superiority of PDP&CGIT.

关键词： dynamic multi-objective optimization Transfer learning Evolutionary algorithms Prediction

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

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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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dynamic multi-objective optimization based on classification response of decision variables

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INFORMATION SCIENCES 2025年 691卷

作者： Li, Jianxia Liu, Ruochen Wang, Ruinan Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

In recent years, many dynamic multi-objective optimization algorithms (DMOAs) have been proposed to address dynamic multi-objective optimization problems (DMOPs). Most existing DMOAs treat all decision variables uniformly and respond to them in an identical manner. This paper proposes a dynamic multi-objective optimization algorithm based on the classification response of decision variables (CRDV-DMO). Firstly, CRDV-DMO categorizes the decision variables into convergence variables and diversity variables. Different decision variables adopt distinct response strategies. The response strategy of diversity variable (RSDV) uses Latin hypercube sampling to generate the diversity variables of the new environment. For each dimensional convergence variable, the response strategy of convergence variable (RSCV) first evaluates whether the basic center prediction strategy (CPS) yields positive feedback or negative feedback, further determining the predictability of that dimensional convergence variable. RSCV then decides to either use the basic CPS to generate the convergence variable for that dimension or to retain that dimensional convergence variable from the current environment, based on the predictability of that dimensional convergence variable. The proposed algorithm is extensively studied through comparison with several advanced DMOAs, demonstrating its effectiveness in dealing with the benchmark DMOPs and the parameter-tuning problem of the PID controller on a dynamic system.

关键词： dynamic multi-objective optimization Classification response Convergence variables Diversity variables PID controller

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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 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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