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INFORMATION SCIENCES 2025年 707卷

作者： Ji, Junzhong Zhang, Xiaoyu Yang, Cuicui Li, Xiang Sui, Guangyuan Beijing Univ Technol Coll Comp Sci Beijing Municipal Key Lab Multimedia & Intelligent Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China

Solving dynamic multiobjective optimization problems (DMOPs) is extremely challenging due to the need to address multiple conflicting objectives that change over time. Transfer prediction- based strategies typically leverage solutions from historical environments to generate an initial population for a new environment. However, these strategies often overlook the similarity between the historical and new environments, which can negatively impact the quality of the initial population. To address this issue, we propose a similar environment transfer strategy. Firstly, we select Pareto-optimal solutions from a randomly generated population in the new environment to form a prior Pareto set (PS). The prior PS is expand by oversampling sparse solutions. Then, we apply the maximum mean discrepancy (MMD) to measure the discrepancy between the prior PS and the PS from each historical environment. The historical environment with the smallest MMD is identified as the similar environment. Finally, we use solutions from this similar environment to establish a kernelized easy transfer learning model, which is employed to predict the quality of random solutions in the new environment. The initial population is formed by combining excellent solutions predicted by the model with the prior PS. Experimental results demonstrate that the proposed strategy significantly outperforms several state-of-the-art strategies.

关键词： dynamic multiobjective optimization Maximum mean discrepancy Transfer learning Prediction-based strategy

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OS-BiTP: Objective sorting-informed bidomain-information transfer prediction for dynamic multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Zhao, Shijie Zhang, Tianran Zhang, Lei Song, Jinling Liaoning Tech Univ Inst Intelligence Sci & Optimizat Fuxin 123000 Peoples R China Liaoning Tech Univ Inst Optimizat & Decis Analyt Fuxin 123000 Peoples R China Liaoning Tech Univ 47 Zhonghua Rd Fuxin 123000 Peoples R China

Prediction response mechanisms based on transfer learning are extensively prevalent in dynamic multiobjective optimization algorithms (DMOAs), which transform historical information into a new environment for tracking the Pareto set (PS) or front (PF). However, many existing methods learn information of overall changes from old to new populations for prediction. Due to the different characteristics of individual variation within the population, this inevitably causes the valid information of more relevant individuals to be partially weakened during the training process, thus reducing transfer prediction-based accuracy. Therefore, this paper proposes an objective sorting-informed bidomain-information transfer prediction (OS-BiTP) for the DMOA based on individual objective variation, with the aim of transferring individuals within the same characteristics. The three core components in OS-BiTP are variation-based objective sorting (VOS), bidomain-information transfer within objective space (BiTOS), and bidomain-information transfer within decision space (BiTDS). Specifically, VOS divides the current PF into high- and low-objective variation classes and designs a modified linear prediction mechanism to forecast new environmental objective vectors. Afterward, VOS trains an easy transfer learning model to match old and new environmental individuals with the same objective variation classes to increase the transfer efficiency of individuals. To accurately track dynamic PFs and PSs, BiTOS and BiTDS perform intraclass correlation alignment for the same class of objective vectors and nondominated solutions and fine-tune the predicted objective vectors and solutions based on their variation differences. The numerical results demonstrate the superior performance and application of OS-BiTP via a systematic comparison with seven state-of-the-art DMOAs.

关键词： dynamic multiobjective optimization Objective sorting Bidomain-information transfer Correlation alignment

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dynamic multiobjective optimization for thrust allocation in ship application

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OCEAN ENGINEERING 2020年 218卷 108187-108187页

作者： Li Xuebin Wuhan 2nd Ship Design & Res Inst Wuhan 430205 Peoples R China

Thrust allocation is a key procedure in the dynamic position system (DPS) of marine vessels. The present work aims to study the characteristics of dynamic optimization in thrust allocation. A two-phase analysis process is proposed. In phase-I, the model for thrust allocation is generated from the viewpoint of multiobjective optimization. Fuel consumption and tear-and-wear on the thrusters are chosen as objectives. multiobjective feasibility enhanced particle swarm optimization algorithm (MOFEPSO) is applied to find the Pareto set of this problem. An additional decision-making procedure, the technique of order preference by similarity to ideal solution (TOPSIS) is utilized to choose the final compromise solution in phase-II. The self-organizing map (SOM) technique is undertaken to mine the Pareto data set. A Remote Operated Vehicle (ROV) example is provided for illustrating the above analysis process. The effects of relative importance between objectives upon characteristics of thrust allocation are studied. The trajectories of decision variables and objectives are examined through the SOM method. Results from numerical examples demonstrate that the multiobjective optimization method together with decision-making skills can extend the application of optimization in the thrust allocation field. The findings in this work add to the understanding of relationships among several aspects of DPS.

关键词： Thrust allocation dynamic multiobjective optimization MOFEPSO Decision-making SOM ROV

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dynamic multiobjective optimization driven by inverse reinforcement learning

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INFORMATION SCIENCES 2021年 575卷 468-484页

作者： Zou, Fei Yen, Gary G. Zhao, Chen Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Northeastern Univ Shenyang 110819 Peoples R China

Due to the widespread interest in dynamic multiobjective optimization in real-world applications, more and more approaches exploiting machine learning are deployed to tackle this type of problems. Unfortunately, recent works do not make full use of the data obtained during the optimization process, which could be benefit for model training thereby mining the dynamic characteristics of the underlying problem. To address this issue, this paper proposes a dynamic multiobjective evolutionary algorithm driven by inverse reinforcement learning to solve the dynamic multiobjective optimization problems. IRL is widely used to recover the unknown reward function, making it possible to perform at an expert level. The notable features of the proposed algorithm mainly consist of data-driven evolutionary technique, which uses inverse reinforcement learning as a surrogate-assisted model for model training. This design makes full use of the surrogate management strategy based on inverse reinforcement learning to optimize the reward function within a reinforcement learning framework. At the same time, the algorithm can generate a promising policy based on limited training data during the optimization process to achieve better algorithm evolution and guide the search. The experimental results on the benchmark problems validate that the proposed algorithm is effective in dealing with dynamic multi objective optimization. (c) 2021 Elsevier Inc. All rights reserved.

关键词： dynamic multiobjective optimization Machine learning Inverse reinforcement learning Evolutionary algorithm Surrogate

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A directed search strategy for evolutionary dynamic multiobjective optimization

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SOFT COMPUTING 2015年第11期19卷 3221-3235页

作者： Wu, Yan Jin, Yaochu Liu, Xiaoxiong Xidian Univ Sch Math & Stat Xian 710071 Peoples R China Univ Surrey Dept Comp Guildford GU2 7XH Surrey England Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

Many real-world multiobjective optimization problems are dynamic, requiring an optimization algorithm that is able to continuously track the moving Pareto front over time. In this paper, we propose a directed search strategy (DSS) consisting of two mechanisms for improving the performance of multiobjective evolutionary algorithms in changing environments. The first mechanism reinitializes the population based on the predicted moving direction as well as the directions that are orthogonal to the moving direction of the Pareto set, when a change is detected. The second mechanism aims to accelerate the convergence by generating solutions in predicted regions of the Pareto set according to the moving direction of the non-dominated solutions between two consecutive generations. The two mechanisms, when combined together, are able to achieve a good balance between exploration and exploitation for evolutionary algorithms to solve dynamic multiobjective optimization problems. We compare DSS with two existing prediction strategies on a variety of test instances having different changing dynamics. Empirical results show that DSS is powerful for evolutionary algorithms to deal with dynamic multiobjective optimization problems.

关键词： dynamic multiobjective optimization Evolutionary algorithm Prediction Local search

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Interaction-Based Prediction for dynamic multiobjective optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第6期27卷 1881-1895页

作者： Liu, Xiao-Fang Xu, Xin-Xin Zhan, Zhi-Hui Fang, Yongchun Zhang, Jun Nankai Univ Inst Robot & Automat Informat Syst Coll Artificial Intelligence Tianjin 300350 Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China Ocean Univ China Sch Comp Sci & Technol Qingdao 266100 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Zhejiang Normal Univ Jinhua 321004 Peoples R China Hanyang Univ Ansan 15588 South Korea

dynamic multiobjective optimization poses great challenges to evolutionary algorithms due to the change of optimal solutions or Pareto front with time. Learning-based methods are popular to extract the changing pattern of optimal solutions for predicting new solutions. They tend to use all variables as features (i.e., inputs) to build prediction models. However, there are usually some irrelevant and redundant variables, which increase training difficulty and decrease prediction accuracy. This article proposes a new interaction-based prediction (IP) method, which captures the correlation of variables with prediction targets and selects the most relevant variables to build prediction models using neural networks. In particular, the interaction between variables is detected to remove redundant variables. In addition, a correction procedure is developed to further improve predicted solutions according to the prediction error in past environments. The predicted solutions are used to update the population according to a specifically designed update strategy. Integrating the IP method into the framework of multiobjective evolutionary algorithm based on decomposition (MOEA/D), a new algorithm named IP-DMOEA is put forward. Experimental results on a typical dynamic multiobjective test suite demonstrate the better performance of the proposed IP-DMOEA than state-of-the-art algorithms in terms of convergence speed and solution quality. The proposed IP-DMOEA is also successfully applied to the multirobot task scheduling problem.

关键词： Correlation dynamic multiobjective optimization evolutionary computation interaction neural network prediction

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Novel prediction and memory strategies for dynamic multiobjective optimization

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SOFT COMPUTING 2015年第9期19卷 2633-2653页

作者： Peng, Zhou Zheng, Jinhua Zou, Juan Liu, Min Xiangtan Univ Informat Engn Coll Minist Educ Key Lab Intelligent Comp & Informat Proc Xiangtan Hunan Peoples R China Hunan Univ Sci & Technol Xiangtan Hunan Peoples R China

dynamic multiobjective optimization problems (DMOPs) exist widely in real life, which requires the optimization algorithms to be able to track the Pareto optimal solution set after the change efficiently. In this paper, novel prediction and memory strategies (PMS) are proposed to solve DMOPs. Regarding prediction, the prediction strategy contains two parts, i.e., exploration and exploitation. Exploration can enhance the ability to search the entire solution space, making it adapt to the environmental change with a great extent. Exploitation can improve the accuracy of local search, making the algorithm to have a faster response to environmental change particularly in the solution set having relevance in the environment. In terms of memory, an optimal solution set preservation mechanism is employed, by reusing the previously found elite solutions, which improves the performance of the algorithm in solving periodic problems. Compared with two representative prediction strategies and a hybrid strategy combining prediction and memory both on seven traditional benchmark problems and on five newly appeared ones, PMS has been shown to have faster response to the environmental changes than the peer algorithms, performing well in terms of convergence and diversity.

关键词： dynamic multiobjective optimization Evolutionary algorithms Prediction Memory

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Multidirectional Prediction Approach for dynamic multiobjective optimization Problems

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IEEE TRANSACTIONS ON CYBERNETICS 2019年第9期49卷 3362-3374页

作者： Rong, Miao Gong, Dunwei Zhang, Yong Jin, Yaochu Pedrycz, Witold China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221006 Jiangsu Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Shandong Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G IH7 Canada

Various real-world multiobjective optimization problems are dynamic, requiring evolutionary algorithms (EAs) to be able to rapidly track the moving Pareto front of an optimization problem once an environmental change occurs. To this end, several methods have been developed to predict the new location of the moving Pareto set (PS) so that the population can be reinitialized around the predicted location. In this paper, we present a multidirectional prediction strategy to enhance the performance of EAs in solving a dynamic multiobjective optimization problem (DMOP). To more accurately predict the moving location of the PS, the population is clustered into a number of representative groups by a proposed classification strategy, where the number of clusters is adapted according to the intensity of the environmental change. To examine the performance of the developed algorithm, the proposed prediction strategy is compared with four state-of-the-art prediction methods under the framework of particle swarm optimization as well as five popular EAs for dynamic multiobjective optimization. Our experimental results demonstrate that the proposed algorithm can effectively tackle DMOPs.

关键词： Adaptation dynamic multiobjective optimization multidirection prediction (MDP) representative individual

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Reducing Negative Transfer Learning via Clustering for dynamic multiobjective optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2022年第5期26卷 1102-1116页

作者： Li, Jianqiang Sun, Tao Lin, Qiuzhen Jiang, Min Tan, Kay Chen Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Fujian Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

dynamic multiobjective optimization problems (DMOPs) aim to optimize multiple (often conflicting) objectives that are changing over time. Recently, there are a number of promising algorithms proposed based on transfer learning methods to solve DMOPs. However, it is very challenging to reduce the negative effect in transfer learning and find more effective transferred solutions. To fill this research gap, this article proposes a clustering-based transfer (CBT) learning method to solve DMOPs. When the environment changes, two novel operations (clustering-based selection (CBS) and CBT) are used to guide knowledge transfer. Specifically, CBS aims to find a population with nondominated solutions and dominated solutions as the training data for the new environment. Then, CBT further collects the previous Pareto-optimal solutions and some noise solutions as the training data for the previous environment. Two training data sets from different environments are, respectively, divided into multiple clusters and transfer learning is conducted on two similar clusters with high probability to reduce the negative effect, which can train an accurate prediction model to identify the promising solutions for the new environment. Empirical studies have been conducted on 14 benchmark DMOPs and one real-life path planning problem of unmanned air/ground vehicles, which validate the effectiveness of our proposed method. Especially, our method can significantly reduce negative transfer on 12 out of 14 cases when compared with direct transfer learning.

关键词： Transfer learning optimization Task analysis Statistics Sociology Predictive models Vehicle dynamics Clustering dynamic multiobjective optimization evolutionary algorithms (EAs) knowledge transfer path planning

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A diversity introduction strategy based on change intensity for evolutionary dynamic multiobjective optimization

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SOFT COMPUTING 2020年第17期24卷 12789-12799页

作者： Liu, Ruochen Peng, Luyao Liu, Jiangdi Liu, Jing Xidian Univ Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

Many real-world problems can be modeled as dynamic multiobjective optimization ones with several competing objectives, which requires an optimization algorithm to track the movement of Pareto front over time. This paper proposes a novel dynamic diversity introduction strategy based on change intensity to improve the performance of dynamic multiobjective optimization based on evolutionary algorithm (DMOEA). In this proposed method, the information generated during evolution is recorded in preparation for evaluating the change intensity. Then, by comparing the evaluated intensity with the inherent intensity, the introduction ratio can be determined by that greater one. Two diversity introduction strategies are utilized to keep the balance between convergence and diversity when environmental change is detected. An improved inverse modeling is used for those drastic changes, while partial solutions random initialization is utilized for these mild ones. We compare the proposed algorithm with four existing DMOEAs on a variety of test instances. The experimental results show that the proposed algorithm exhibits better search performance.

关键词： dynamic multiobjective optimization Diversity introduction strategy Inverse model Evolutionary algorithm

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