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A dual prediction strategy with inverse model for evolutionary dynamic multiobjective optimization

ISA TRANSACTIONS 2021年 117卷 196-209页

作者： Li, Xiaxia Yang, Jingming Sun, Hao Hu, Ziyu Cao, Anran Yanshan Univ Minist Educ Intelligent Control Syst & Intelligen Engn Res Ctr Qinhuangdao Hebei Peoples R China Yanshan Univ Inst Elect Engn Qinhuangdao Hebei Peoples R China

In practical applications and daily life, dynamic multiobjective optimization problems (DMOPs) are ubiquitous. The purpose of dealing with DMOPs is to track moving Pareto Front (PF) and find a series of Pareto Set (PS) at different times. Prediction-based strategies improve the performance of multiobjective evolutionary algorithms in dynamic environments. However, how to ensure the accuracy of prediction models is always a challenge. In this study, a dual prediction strategy with inverse model (DPIM) is developed, to alleviate the negative impact of inaccurate prediction. When a change is confirmed, DPIM responses to it by predicting the individuals in the objective space. Furthermore, the inverse model is established to connect the decision space with the objective space, which can guide the search for promising decision areas. Specifically, the inverse model is also predicted to minimize the error in the process of mapping the population from the objective space back to the decision space. The effectiveness of the proposed DPIM is proved by comparison with four effective DMOEAs on 14 benchmark problems with various real-word scenarios. The experimental results show that DPIM can obtain high-quality populations with good convergence and distribution in dynamic environments. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Dual prediction strategy dynamic multiobjective optimization Inverse model

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Co-evolutionary algorithm based on problem analysis for dynamic multiobjective optimization

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INFORMATION SCIENCES 2023年第1期634卷 520-538页

作者： Li, Xiaoli Cao, Anran Wang, Kang Li, Xin Liu, Quanbo Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China

dynamic multiobjective optimization problems (DMOPs) vary over time, requiring an optimization algorithm to track the position of Pareto-optimal front (PF) in a dynamic environment. To achieve that, a novel co-evolutionary algorithm based on problem analysis (CAPA) is proposed in this paper. CAPA is designed to solve DMOPs from decision space and objective space simultaneously, which is achieved by the combination of adjustable prediction (AP) and precise mapping strategy (PM). In decision space, the proposed multi-model prediction can estimate the location of new population based on the historical median points. In objective space, a novel sampling method is developed to search for sample points with better convergence or diversity. Then, mapping these sample points back to decision space based on inverse model. Through the problem analysis mechanism, the proportion of the new solutions produced by each strategy changes adaptively. CAPA is incorporated into the dynamic multiobjective evolutionary algorithm (DMOEA) based on decomposition (MOEA/D) to construct a novel algorithm. The efficacy of CAPA is validated by comparison with five state-of-the-art algorithms on 28 benchmarks. Experimental results show that CAPA has the ability to generate high quality population uniformly along PF.

关键词： dynamic multiobjective optimization Problem analysis Prediction Sampling method

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Interindividual Correlation and Dimension-Based Dual Learning for dynamic multiobjective optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第6期27卷 1780-1793页

作者： Yan, Li Qi, Wenlong Liang, Jing Qu, Boyang Yu, Kunjie Yue, Caitong Chai, Xuzhao Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China

dynamic multiobjective optimization problems (DMOPs) are characterized by their multiple objectives, constraints, and parameters that may change over time. The challenge in solving DMOPs is how to track the varying Pareto-optimal solution sets quickly and accurately. Therefore, an interindividual correlation and dimension-based dual-learning method is proposed in this article. Two learning strategies, decomposition-based interindividual correlation transfer learning (DICTL) and dimension-wise learning (DL), are developed to, respectively, generate one-half of the initial population in the new environment. More specifically, DICTL learns the interindividual correlation from the final population of the adjacent environment and then transfers it to the new environment, aiming to maintain the diversity and distribution of the predicted population. While DL extracts the changing pattern of dynamic environments from the high-quality solutions of historical environments in the perspective of variable dimension, trying to improve the quality of the population and accelerate the convergence. The designed two learning strategies (DICTL&DL) work complementarily and collaboratively to make the algorithm adapt to dynamic environments better and faster. Comprehensive experiments have been conducted by comparing the proposed method with four state-of-the-art algorithms on 14 benchmark problems. The results demonstrate the superiority of the proposed method.

关键词： Sociology Correlation optimization Convergence Heuristic algorithms Prediction algorithms Manifolds Dimension dual learning dynamic multiobjective optimization interindividual correlation

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A Knowledge Guided Transfer Strategy for Evolutionary dynamic multiobjective optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第6期27卷 1750-1764页

作者： Guo, Yinan Chen, Guoyu Jiang, Min Gong, Dunwei Liang, Jing China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China Xiamen Univ Dept Cognit Sci & Technol Xiamen 361005 Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China

The key task in dynamic multiobjective optimization problems (DMOPs) is to find Pareto-optima closer to the true one as soon as possible once a new environment occurs. Previous dynamic multiobjective evolutionary algorithms (DMOEAs) normally focus on DMOPs with regular environmental changes, but neglect widespread random one, limiting their applications in real-world fields. To address this issue, a knowledge guided transfer strategy (KTS)-based DMOEA is proposed in this article. First, knowledge described as a two-tuple is extracted under each historical environment and preserved to a knowledge pool. Redundant knowledge is recognized and adaptively removed so as to guarantee the diversity of the pool. Second, a knowledge matching strategy is developed to re-evaluate the representative of each stored knowledge under a new environment, with the purpose of finding the most valuable one to promote positive knowledge transfer. Third, an improved knowledge transfer mechanism based on subspace alignment is introduced. By integrating it with the knowledge reuse mechanism, a hybrid transfer strategy is constructed to adaptively select the most suitable one in terms of the similarity degree of selected knowledge on the current environment, and then generate a new initial population. Experiments on 20 benchmark problems demonstrate that the KTS outperforms five state-of-the-art algorithms, achieving good versatility in solving DMOPs with both regular and random changes.

关键词： Statistics Sociology optimization Heuristic algorithms Transfer learning Evolutionary computation Manifolds dynamic multiobjective optimization knowledge transfer random change regular change

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Individual-Based Transfer Learning for dynamic multiobjective optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第10期51卷 4968-4981页

作者： Jiang, Min Wang, Zhenzhong Guo, Shihui Gao, Xing Tan, Kay Chen Xiamen Univ Sch Informat Xiamen 361005 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen 518057 Peoples R China

dynamic multiobjective optimization problems (DMOPs) are characterized by optimization functions that change over time in varying environments. The DMOP is challenging because it requires the varying Pareto-optimal sets (POSs) to be tracked quickly and accurately during the optimization process. In recent years, transfer learning has been proven to be one of the effective means to solve dynamic multiobjective optimization. However, the negative transfer will lead the search of finding the POS to a wrong direction, which greatly reduces the efficiency of solving optimization problems. Minimizing the occurrence of negative transfer is thus critical for the use of transfer learning in solving DMOPs. In this article, we propose a new individual-based transfer learning method, called an individual transfer-based dynamic multiobjective evolutionary algorithm (IT-DMOEA), for solving DMOPs. Unlike existing approaches, it uses a presearch strategy to filter out some high-quality individuals with better diversity so that it can avoid negative transfer caused by individual aggregation. On this basis, an individual-based transfer learning technique is applied to accelerate the construction of an initial population. The merit of the IT-DMOEA method is that it combines different strategies in maintaining the advantages of transfer learning methods as well as avoiding the occurrence of negative transfer;thereby greatly improving the quality of solutions and convergence speed. The experimental results show that the proposed IT-DMOEA approach can considerably improve the quality of solutions and convergence speed compared to several state-of-the-art algorithms based on different benchmark problems.

关键词： Sociology Statistics optimization Heuristic algorithms Prediction algorithms Predictive models Convergence dynamic multiobjective optimization evolutionary algorithm prediction transfer learning

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Inverse Gaussian Process Modeling for Evolutionary dynamic multiobjective optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第10期52卷 11240-11253页

作者： Zhang, Huan Ding, Jinliang Jiang, Min Tan, Kay Chen Chai, Tianyou Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Xiamen Univ Dept Artificial Intelligence Xiamen 361005 Fujian Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

For dynamic multiobjective optimization problems (DMOPs), it is challenging to track the varying Pareto-optimal front. Most traditional approaches estimate the Pareto-optimal sets in the decision space. However, the obtained solutions do not necessarily satisfy the desired properties of decision makers in the objective space. Inverse model-based algorithms have a great potential to solve such problems. Nonetheless, the existing ones have low precision for handling DMOPs with nonlinear correlations between the objective and decision vectors, which greatly limits the application of the inverse models. In this article, an inverse Gaussian process (IGP)-based prediction approach for solving DMOPs is proposed. Unlike most traditional approaches, this approach exploits the IGP to construct a predictor that maps the historical optimal solutions from the objective space to the decision space. A sampling mechanism is developed for generating sample points in the objective space. Then, the IGP-based predictor is employed to generate an effective initial population by using these sample points. The proposed method by introducing IGP can obtain solutions with better diversity and convergence in the objective space, which is more responsive to the demand of decision makers than the traditional methods. It also has better performance than other inverse model-based methods in solving nonlinear DMOPs. To investigate the performance of the proposed approach, experiments have been conducted on 23 benchmark problems and a real-world raw ore allocation problem in mineral processing. The experimental results demonstrate that the proposed algorithm can significantly improve the dynamic optimization performance and has certain practical significance for solving real-world DMOPs.

关键词： Inverse problems optimization Heuristic algorithms Computational modeling Gaussian processes Kernel Sociology dynamic multiobjective optimization evolutionary algorithm (EA) inverse Gaussian process (IGP) objective space prediction

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Solving dynamic multiobjective optimization Problems via Feedback-Guided Transfer and Trend Manifold Prediction

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第12期54卷 7218-7229页

作者： Wang, Yong Li, Kuichao Wang, Gai-Ge Gong, Dunwei Li, Keqin Ocean Univ China Sch Comp Sci & Technol Qingdao 266101 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266101 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Solving dynamic multiobjective optimization problems (DMOPs) is very challenging due to the requirements to respond rapidly and precisely to changes in an environment. Many prediction-and memory-based algorithms have been recently proposed for meeting these requirements. However, much useful knowledge has been ignored during the historical search process, and prediction deviations could occur, thus limiting the applicability of these methods to a variety of problems. Facing these concerns, this article proposes an evolutionary algorithm named FGTTMP based on feedback-guided transfer (FGT) and trend manifold prediction (TMP) for solving DMOPs. The FGT employs an information feedback model to extract valuable knowledge using all historical environments and then identifies excellent individuals using cluster-transfer learning. This can both accelerate convergence and introduce diversity for future environments. The TMP applies the probability-based trend prediction method to estimate the mass center of the whole population and the corresponding manifold, relying on the two previous moments. Thus, the FGT and TMP strategies combine historical knowledge with prediction techniques, synergistically leading the population in promising directions. The performance of the proposed algorithm is fully investigated and compared with eight state-of-the-art algorithms. Experimental results demonstrate that the proposed FGTTMP method can achieve better convergence and diversity on 19 various benchmark problems than the state-of-the-art algorithms.

关键词： dynamic multiobjective optimization feedback-guided transfer (FGT) knowledge trend manifold prediction (TMP)

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Immune clonal coevolutionary algorithm for dynamic multiobjective optimization

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NATURAL COMPUTING 2014年第3期13卷 421-445页

作者： Shang, Ronghua Jiao, Licheng Ren, Yujing Wang, Jia Li, Yangyang Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China

In this paper, a new evolutionary algorithm, called immune clonal coevolutionary algorithm (ICCoA) for dynamic multiobjective optimization (DMO) is proposed. On the basis of the basic principles of artificial immune system, the proposed algorithm adopts the immune clonal selection to solve DMO problems. In addition, the theory of coevolution is incorporated in ICCoA in global operation to preserve the diversity of Pareto-fronts. Moreover, coevolutionary competitive and cooperative operation is designed to enhance the uniformity and the diversity of the solutions. In comparison with NSGA-II, immune clonal algorithm for DMO and direction-based method, the simulation results obtained on 5 difficult test problems and on related performance metrics suggest that ICCoA can achieve better distributed solutions and be very effective in maintaining the uniformity of Pareto-fronts.

关键词： dynamic multiobjective optimization Immune clonal selection Coevolution

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A Mahalanobis Distance-Based Approach for dynamic multiobjective optimization With Stochastic Changes

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2024年第1期28卷 238-251页

作者： Hu, Yaru Zheng, Jinhua Jiang, Shouyong Yang, Shengxiang Zou, Juan Wang, Rui Xiangtan Univ Key Lab Hunan Prov Internet Things & Informat Secu Xiangtan 411105 Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Peoples R China Univ Aberdeen Dept Comp Sci Aberdeen AB24 3FX Scotland De Montfort Univ Inst Artificial Intelligence Sch Comp Sci & Informat Leicester LE1 9BH England Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China

In recent years, researchers have made significant progress in handling dynamic multiobjective optimization problems (DMOPs), particularly for environmental changes with predictable characteristics. However, little attention has been paid to DMOPs with stochastic changes. It may be difficult for existing dynamic multiobjective evolutionary algorithms (DMOEAs) to effectively handle this kind of DMOPs because most DMOEAs assume that environmental changes follow regular patterns and consecutive environments are similar. This article presents a Mahalanobis distance-based approach (MDA) to deal with DMOPs with stochastic changes. Specifically, we make an all-sided assessment of search environments via Mahalanobis distance on saved information to learn the relationship between the new environment and historical ones. Afterward, a change response strategy applies the learning to the new environment to accelerate the convergence and maintain the diversity of the population. Besides, the change degree is considered for all decision variables to alleviate the impact of stochastic changes on the evolving population. An MDA has been tested on stochastic DMOPs with two to four objectives. The results show that MDA performs significantly better than the other latest algorithms in this article, suggesting that MDA is effective for DMOPs with stochastic changes.

关键词： optimization Statistics Sociology Convergence Stochastic processes Maintenance engineering dynamic scheduling Algorithms dynamic multiobjective optimization Mahalanobis distance (MD) stochastic changes

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Hybrid of memory and prediction strategies for dynamic multiobjective optimization

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INFORMATION SCIENCES 2019年 485卷 200-218页

作者： Liang, Zhengping Zheng, Shunxiang Zhu, Zexuan Yang, Shengxiang Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Cyberspace Lab Shenzhen 518052 Peoples R China De Montfort Univ CCI Sch Comp Sci & Informat Leicester LE1 9BH Leics England

dynamic multiobjective optimization problems (DMOPs) are characterized by a time-variant Pareto optimal front (PF) and/or Pareto optimal set (PS). To handle DMOPs, an algorithm should be able to track the movement of the PF/PS over time efficiently. In this paper, a novel dynamic multiobjective evolutionary algorithm (DMOEA) is proposed for solving DMOPs, which includes a hybrid of memory and prediction strategies (HMPS) and the multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-HMPS) detects environmental changes and identifies the similarity of a change to the historical changes, based on which two different response strategies are applied. If a detected change is dissimilar to any historical changes, a differential prediction based on the previous two consecutive population centers is utilized to relocate the population individuals in the new environment;otherwise, a memory-based technique devised to predict the new locations of the population members is applied. Both response mechanisms mix a portion of existing solutions with randomly generated solutions to alleviate the effect of prediction errors caused by sharp or irregular changes. MOEA/D-HMPS was tested on 14 benchmark problems and compared with state-of-the-art DMOEAs. The experimental results demonstrate the efficiency of MOEA/D-HMPS in solving various DMOPs. (C) 2019 Elsevier Inc. All rights reserved.

关键词： dynamic multiobjective optimization Evolutionary algorithms Similar environment Memory Prediction

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