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Solving dynamic multi-objective optimization problem of immersed tunnel elements via multi-source evolutionary information clustering method

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 140卷

作者： Fan, Qinqin Huang, Wentao Yu, Moduo Tang, Qirong Jiang, Qingchao Shanghai Maritime Univ Logist Res Ctr Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Shanghai 200240 Peoples R China Tongji Univ Lab Robot & Multibody Syst Shanghai 200092 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

dynamic multi-objective optimization problems (DMOPs) are time- and space-varying, thus maintaining/ improving the uncertainty degree of evolutionary information (i.e., information entropy) in the population and providing useful knowledge are two important tasks to make dynamic multi-objective evolutionary algorithms (DMOEAs) adapt to changing environments. To achieve the above objectives, a multi-source population clustering (MPC) method is proposed to assist DMOEAs in improving their tracking performance during the full-cycle optimization in the current study. In the MPC, three different information sources are used to provide diverse spatiotemporal evolutionary information, aiding DMOEAs in adapting to various changing environments. Subsequently, an enhanced spectral clustering approach is employed to group all evolutionary individuals from different information sources into many clusters/subspaces. Finally, the selected DMOEA is employed to search all subspaces in parallel via the high-performing computing method. The MPC is incorporated into a regularity model-based multi-objective estimation of distribution algorithm (called as MPC-RM-MEDA) and is compared with six famous DMOEAs on 14 10- and 30-dimensional DMOPs, which are proposed in IEEE Congress on Evolutionary computation 2018. Experimental results demonstrate that the overall tracking performance of the proposed MPC-RM-MEDA is significantly superior to that of other selected competitors in various dynamic environments. Additionally, the MPC-RM-MEDA is utilized to address a real-world DMOP involving an immersed tunnel element. The obtained results and comparison with the knee point-based transfer learning method verify that the MPC is an efficient and dependable approach for enhancing the tracking performance of other DMOEAs in solving actual DMOPs.

关键词： dynamic multi-objective optimization Evolutionary computation High-performing computing multi-source information Immersed tunnel element

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Division-selection transfer learning for prediction based dynamic multi-objective optimization

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COMPLEX & INTELLIGENT SYSTEMS 2025年第1期11卷 1-21页

作者： Li, Hongye Liang, Fan Liu, Yulu Zheng, Quanheng Guo, Kunru Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Xian Key Lab Big Data & Intelligent Comp Xian 710121 Shaanxi Peoples R China

dynamic multi-objective optimization problems (DMOPs) are challenging as they require capturing the Pareto optimal front (POF) and Pareto optimal set (POS) during the optimization process. In recent years, transfer learning (TL) has emerged the empirical knowledge and is an effective approach to solve DMOPs. However, negative transfer can occur when the transfer method is not suitable for the transfer task. It may deviate the search path and seriously reduce the efficiency, so how to reduce the occurrence of negative transfer to save the running time of dynamic multi-objective evolutionary algorithm (DMOEA) is an important issue to be addressed. A division-selection transfer learning evolutionary algorithm for dynamic multi-objective optimization (DST-DMOEA) is designed towards this aim. Specifically, individuals with high Spearman correlation are relatively stable in different environments, selecting them to train the Support Vector Regression (SVR) model ensures a more accurate capture of solution features, predicting the objective values of historical solutions based on the model, and thus divide historical solutions into elite and non-elite solutions. Subsequently, for the elite solutions, individual TL that incorporates local information for optimization and transfer is used, while the non-elite solutions are handled with manifold TL method to obtain the overall data distribution and understand the internal structure. Then, merge the predicted individuals generated by two parts of TL will constitute as the initial population in the optimization process. Compared with other algorithms, the initial solution of DST-DMOEA is closer to the real POF, effectively reducing negative transfer. In addition, in 51 test instances, DST-DMOEA has shown superior performance in over 30 instances.

关键词： dynamic multi-objective optimization Division-selection External sampling Transfer learning Negative transfer Evolutionary algorithms

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Knowledge hierarchy-based dynamic multi-objective optimization method for AUV path planning in cooperative search missions

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OCEAN ENGINEERING 2024年第Part3期312卷

作者： Wang, Yinhuan Liu, Kaizhou Geng, Lingbo Zhang, Shaoze Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This study focuses on path planning for Autonomous Underwater Vehicles (AUVs) in underwater cooperative search missions. The complexities of the ocean environment, the uncertainty of target movements, and limited communication capabilities render underwater cooperative search missions dynamic multi-objective optimization challenges. Studies focusing on communication-constrained search strategies still lack reliable metrics for assessing underwater communication quality and do not consider communication dead zones. Addressing these deficiencies, this paper introduces a novel communication optimization strategy utilizing packet error rate as a metric for assessing communication efficacy, complemented by a potential field-based method for navigating out of communication dead zones. To tackle the inherently dynamic nature of cooperative search missions for mobile underwater targets, we propose a dynamic multi-objective optimization algorithm that employs a knowledge hierarchy strategy. This method enhances the NSGA-II algorithm's efficiency by extracting effective gene segments from historical Pareto solution set and generating a new initial population through recombination, hierarchy, and prediction. Distinct from other advanced dynamic multi-objective optimization approaches that are limited to theoretical problems, our approach is directly applicable to practical scenarios. The effectiveness and practicality of the proposed method are validated through a series of simulation experiments considering the impact of underwater acoustic communication. These results demonstrate that this research is not only innovative in theory but also holds significant engineering value and practical prospects in real-world applications.

关键词： Autonomous underwater vehicle (AUV) Cooperative search dynamic multi-objective optimization NSGA-II Restricted communication Underwater acoustic channel model

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An adaptive Gaussian process based manifold transfer learning to expensive dynamic multi-objective optimization

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NEUROCOMPUTING 2023年第1期538卷

作者： Zhang, Xi Yu, Guo Jin, Yaochu Qian, Feng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Nanjing Tech Univ Inst Intelligent Mfg Nanjing 211816 Peoples R China Bielefeld Univ Fac Technol Chair Nat Inspired Comp & Engn D-33619 Bielefeld Germany Univ Surrey Dept Comp Sci Guildford GU2 7XH England

Expensive dynamic multi-objective optimization problems (EDMOPs) is one kind of DMOPs where the objectives change over time and the function evaluations commonly involve computationally intensive simulations or costly physical experiments. Hence, the key to solve EDMOPs is to quickly and accurately track the time-varying Pareto optimal fronts under the limit of small number of function evaluations, in which how to augment enough training data to build informative surrogate models and manage the mod-els during the search process. To overcome the issue, we propose a transfer learning based surrogate assisted evolutionary algorithm (TrSA-DMOEA) to efficiently solve EDMOPs. Specifically, when a change occurs, we propose a knee point-based manifold transfer learning method based on geodesic flow kernel, which exploits the knowledge from previous high-quality knee solutions to augment the training data for building Gaussian process models, thereby improving the computational complexity and the quality of solutions. Moreover, to efficiently find the optima with limited budget of function evaluations, a novel surrogate-assisted mechanism based on an adaptive acquisition function is introduced, which achieves a balance between convergence and diversity by adaptively adjusting the weights of the angle -penalized distance and average uncertainty at different search stages. By comparing with state-of-the-art algorithms on widely used test problems, the experimental results demonstrate that the proposed method outperforms others and is able to efficiently solve EDMOPs.(c) 2023 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization Transfer learning Gaussian process Surrogate assisted evolutionary algorithm

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A new hybrid prediction model with entropy-like kernel function for dynamic multi-objective optimization

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APPLIED INTELLIGENCE 2023年第9期53卷 10500-10519页

作者： Cao, Siyu Zou, Feng Chen, Debao Liu, Hui Ji, Xuying Zhang, Yan Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China

dynamic multi-objective problems (DMOPs) permeate all aspects of daily life and practical applications. As the variables of the search space or target space alter in pace with time, savants are also deepening the research on DMOPs, among which methods based on prediction mechanisms have been extensively developed. The historical optimal solutions can effectively predict the trend and location of the optimal solutions in the future. In this paper, a new hybrid prediction model (HPM) integrating the fuzzy linear prediction model with entropy-like kernel function and the one-step prediction model is developed to sort out DMOPs. In the method, the predicted center by the HPM prediction model is combined with the approximate manifold of PS to generate a trail population, and the linear one-step prediction model is utilized to generate another trail population. When the environment changes, the initial PS at the next moment is obtained by randomly crossing these two trail populations. To assess the proposed HPM model, it is compared with the reinitialization strategy, feedforward prediction strategy, population prediction strategy, T-S nonlinear regression strategy with multistep prediction and individual-based transfer learning under different MOEA optimizers for 22 benchmark problems. The results indicate that HPM has great advantages in solving these dynamic optimization problems.

关键词： dynamic multi-objective optimization Fuzzy linear prediction Entropy-like kernel function multiple subgroups One-step prediction

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multi-spatial information joint guidance evolutionary algorithm for dynamic multi-objective optimization with a changing number of objectives

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NEURAL COMPUTING & APPLICATIONS 2023年第20期35卷 15167-15199页

作者： Ma, Xuemin Sun, Hao Hu, Ziyu Wei, Lixin Yang, Jingming Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligent Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

Existing research on dynamic multi-objective optimization problems involving changes in the number of objectives has received little attention, but it is widespread in practical applications. This problem would cause the expansion or contraction of the manifold in the objective space. If it is accompanied by changes in Pareto set/front (PS/PF), the problem becomes more complex. However, several dynamic response techniques have been developed to handling this kind of dynamics. Faced with these issues, a multi-spatial information joint guidance evolutionary algorithm is proposed. To more accurately identify the optimal solutions after the change, a space adaptive transfer strategy is introduced, which adopts the geodesic flow kernel method to extract spatial information at different times. Afterwards it adaptively transfers the space via different changes to generate new individuals. In order to improve the diversity after the change, a dual space multi-dimensional joint sampling strategy is proposed. It fully combines the individual information in the objective and the decision space. Then the promising solutions are sampled in multiple dimensions near the representative individuals. Comprehensive experiments are conducted on 15 benchmark functions with a varying number of objectives and PS/PF. Simulation results verify the capability of the proposed algorithm.

关键词： dynamic multi-objective optimization multi-objective optimization Changing objectives Evolutionary algorithm

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Cluster-Based Regression Transfer Learning for dynamic multi-objective optimization

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PROCESSES 2023年第2期11卷 613页

作者： Zhang, Xi Qian, Feng Zhang, Liping East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Shanxi Agr Univ Inst Cotton Res Yuncheng 044000 Peoples R China

Many multi-objective optimization problems in the real world have conflicting objectives, and these objectives change over time, known as dynamic multi-objective optimization problems (DMOPs). In recent years, transfer learning has attracted growing attention to solve DMOPs, since it is capable of leveraging historical information to guide the evolutionary search. However, there is still much room for improvement in the transfer effect and the computational efficiency. In this paper, we propose a cluster-based regression transfer learning-based dynamic multi-objective evolutionary algorithm named CRTL-DMOEA. It consists of two components, which are the cluster-based selection and cluster-based regression transfer. In particular, once a change occurs, we employ a cluster-based selection mechanism to partition the previous Pareto optimal solutions and find the clustering centroids, which are then fed into autoregression prediction model. Afterwards, to improve the prediction accuracy, we build a strong regression transfer model based on TrAdaboost.R2 by taking advantage of the clustering centroids. Finally, a high-quality initial population for the new environment is predicted with the regression transfer model. Through a comparison with some chosen state-of-the-art algorithms, the experimental results demonstrate that the proposed CRTL-DMOEA is capable of improving the performance of dynamic optimization on different test problems.

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

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An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems

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COMPLEX & INTELLIGENT SYSTEMS 2023年第1期9卷 659-675页

作者： Chen, Meirong Guo, Yinan Jin, Yaochu Yang, Shengxiang Gong, Dunwei Yu, Zekuan China Univ Min & Technol Sch Math Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England Fudan Univ Huashan Hosp Dept Radiol Shanghai 200040 Peoples R China

In dynamic multi-objective optimization problems, the environmental parameters may change over time, which makes the Pareto fronts shifting. To address the issue, a common idea is to track the moving Pareto front once an environmental change occurs. However, it might be hard to obtain the Pareto optimal solutions if the environment changes rapidly. Moreover, it may be costly to implement a new solution. By contrast, robust Pareto optimization over time provides a novel framework to find the robust solutions whose performance is acceptable for more than one environment, which not only saves the computational costs for tracking solutions, but also minimizes the cost for switching solutions. However, neither of the above two approaches can balance between the quality of the obtained non-dominated solutions and the computation cost. To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions.

关键词： Changing environment Tracking moving optimum Robust Pareto-optima over time dynamic multi-objective optimization

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A special point-based transfer component analysis for dynamic multi-objective optimization

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COMPLEX & INTELLIGENT SYSTEMS 2023年第2期9卷 1229-1245页

作者： Liu, Ruochen Li, Nanxi Peng, Luyao Wu, Kai Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Int Ctr Intelligent Percept & Computat Xian 710071 Peoples R China

To solve dynamic multi-objective optimization problems better, the key is to adapt quickly to environmental changes and track the possible changing optimal solutions in time. In this paper, we propose a special point-based transfer component analysis for dynamic multi-objective optimization algorithm (SPTr-RM-MEDA). To be specific, when a change occurs, the neighbors of some special points are selected from the optimal set at previous time, and the transfer component analysis makes the use of minimizing the distance between the mapped previous optima and the mapped current optima. Accordingly, the purpose is to predict a part of next initial population from the neighborhoods of special points by transfer component analysis. To adapt to the change well, SPTr-RM-MEDA also reevaluates the previous optimal set. In addition, an adaptive diversity introduction strategy is adopted to maintain the population size. SPTr-RM-MEDA is performed on 12 test problems under 8 kinds of environmental changes, and experimental results show that it is superior to other five state-of-the-art algorithms on most of test problems.

关键词： Special point Transfer component analysis dynamic multi-objective optimization

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An Adaptive Knowledge Transfer Strategy for Evolutionary dynamic multi-objective optimization 18th

An Adaptive Knowledge Transfer Strategy for Evolutionary Dyn...

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18th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

作者： Zhao, Donghui Lu, Xiaofen Tang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China

ISBN: (纸本)9789819722716;9789819722723

dynamic multi-objective optimization problems (DMOPs) are optimization problems involve multiple conflicting objectives, and these objectives change over time. The challenge in solving DMOPs is how to quickly track the Pareto optimal solution set when the environment changes. Recently, dynamic multi-objective evolutionary algorithms (DMOEAs) combined with transfer learning (TL) have been proven to be promising in solving DMOPs. TL-based DMOEAs showed advantages in reusing historical information and predicting high-quality solutions in the new environment. Various TL techniques have been employed to DMOEAs, which learn and transfer knowledge either in decision space or in objective space to predict the Pareto optimal solutions. However, problems usually have different types of change in decision and objective spaces. A single knowledge learning and transfer strategy may be unsuitable for all types of DMOPs. In this paper, a DMOEA with an adaptive knowledge learning and transfer strategy is proposed to solve DMOPs. It first estimates the change type of the problem when the environment changes, i.e., whether there exists change in decision or objective spaces, and then based on the change type, it adaptively chooses to learn and transfer knowledge in the decision space or objective space or both to generate an initial population that guides the search in new environment. A comprehensive empirical study is conducted to evaluate the performance of the proposed method. The method is compared to six state-of-the-art prediction-based DMOEAs on widely used DMOP benchmarks. Experimental results demonstrate that the proposed method outperforms or achieves comparable results to the compared algorithms on most of the test problems.

关键词： dynamic multi-objective optimization Evolutionary algorithm Transfer learning Change type

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