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6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Li, Suixun Zou, Feng Chen, Debao Huang, Huimei Huaibei Normal Univ Sch Phys & Elect Informat Anhui Prov Key Lab Intelligent Comp & Applicat Huaibei Peoples R China

ISBN: (纸本)9798350377859;9798350377842

dynamic multi-objective optimization problems (DMOPs) exist widely in real life, and the algorithm needs to track changing Pareto optima accurately and quickly after the environment changes when dealing with these DMOPs. In response to the above issues, this paper proposes a prediction strategy assisted by long short-term memory recurrent neural network (PS-LSTM) for DMOPs. In the proposed method, when environmental changes occur, the difference of the center point of Pareto solutions is predicted by long short-term memory recurrent neural network and then the evolutionary direction of Pareto solutions can be calculated to better track changing Pareto optima by selecting excellent individuals in a new environment. Moreover, the next initial population is predicted based on the difference of the center point and the evolutionary direction of Pareto solutions. Finally, the PS-LSTM strategy is compared with four representative strategies on 11 benchmark test problems. Experimental results indicate that the strategy has better performance.

关键词： dynamic multi-objective optimization LSTM New environmental information

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dynamic multi-objective optimization and fuzzy AHP for copper removal process of zinc hydrometallurgy

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Zhou, Xiaojun Sun, Yan Huang, Zhaoke Yang, Chunhua Yen, Gary G. Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

In order to improve the production efficiency and reduce the production cost of copper removal process (CRP), it is necessary to control the addition rate of zinc powder in CRP. In this study, the control of zinc powder addition rate is modeled as a dynamic multi-objective optimization problem (DMOP). A Pareto set which contains multiple control schemes is obtained by using multi-objective state transition algorithm based on decomposition, and then a new fuzzy multi-criteria decision-making (MCDM) is proposed to select the best control scheme. In the MCDM, according to the real-world requirements of CRP, four criteria are adopted to evaluate these schemes, and the fuzzy analytic hierarchy process (AHP) is adopted to determine the criteria weights according to a fuzzy comparison matrix. Furthermore, the nonlinear mixed integer programming model is established to guarantee consistency of the fuzzy comparison matrix. Experimental results of an industrial plant in China have demonstrated the effectiveness and superiority of the proposed method in terms of the cost, reaction rate, stability of outlet copper ion concentration, etc. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Copper removal process Continuous stirred-tank reactor (CSTR) dynamic multi-objective optimization Fuzzy AHP Fuzzy logic

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A Joint Prediction Strategy Based on multiple Feature Points for dynamic multi-objective optimization 15th

A Joint Prediction Strategy Based on Multiple Feature Points...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Li, Yaxin Yan, Li Yu, Kunjie Liang, Jing Qu, Boyang Zhongyuan Univ Technol Zhengzhou Peoples R China Zhengzhou Univ Zhengzhou Peoples R China

ISBN: (纸本)9789819771806;9789819771813

This paper proposes a joint prediction strategy based on multiple feature points (JP-MFP) to improve the prediction accuracy and balance the convergence and diversity. On the one hand, a multi-step prediction based on center points is designed to generate some predicted individuals around the potential area of the new Pareto optimal Set (POS), which expands the coverage area of the predicted population and enhances its diversity. On the other hand, a multidirectional prediction based on extreme and knee points is proposed in which the nondominated solutions are firstly clustered based on the extreme points and knee points, and then different movement directions are assigned to each cluster. Resultantly, it can predict the new POS from multiple directions and improve the distribution of the population. Further, the convergence can be accelerated. Finally, the predicted sub-populations obtained by the two strategies are selected according to non-dominated sorting and crowding distance to generate a joint predicted population for the new environment. Experimental results demonstrate that the proposed JP-MFP can effectively solve DMOPs by comparing with several advanced algorithms on the DF test problems.

关键词： dynamic multi-objective optimization Joint Prediction Feature Points

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Individual-based self-learning prediction method for dynamic multi-objective optimization

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INFORMATION SCIENCES 2022年 613卷 401-418页

作者： Ou, Junwei Li, Mengjun Xing, Lining Lv, Jimin Hu, Yaru Dong, Nanjiang Zhang, Guoting Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Hunan Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China

This paper proposes an individual-based self-learning prediction method for dynamic multi-objective optimization problems, called ISPM, to effectively track the time-varying Pareto-optimal set (POS) in a dynamic environment. The ISPM adjusts the reference points by individual-based self-learning, which differs from existing approaches based on fixed reference points. The self-learning reference points are given according to the information from the previous population to divide the Pareto-optimal front (POF) into the objective space as uniformly as possible. One of the ISPM's advantages is it can improve the influence of the corresponding non-uniform POF for the population's prediction. Furthermore, it is known that each reference point and the original point can form a vector in the objective space. Each vector can present a subregion in the objective space. The moving direction of each subregion in the last two environments is the reference when improving the popula-tion in the new environment to realize local search. Meanwhile, we roughly calculate the change degree of the environment using the self-learning reference point sets at the last two environments to realize the global search. The comprehensive experimental results show that the proposed algorithm can effectively balance convergence and diversity com-pared with other state-of-the-art methods.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Evolutionary algorithms dynamic multi-objective optimization Prediction method Self-learning reference points

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An Iterative Machine Learning Approach to Informative Performance Reporting in dynamic multi-objective optimization

An Iterative Machine Learning Approach to Informative Perfor...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Herring, Daniel Kirley, Michael Yao, Xin Univ Melbourne Melbourne Vic Australia Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9798400701207

dynamic multi-objective optimization problems (DMOPs) can represent formulations of complex realistic scenarios in industrial, logistics and energy domains. Consistent and comparable testing on DMOP benchmarks has seen recent progress in tools for comprehensive and reproducible testing. Ranges of possible dynamic instances of DMOPs can be generated defined by their frequency and severity of change parameters. Here, a combination of machine learning and evolutionary algorithm techniques allow for an efficient determination of algorithm performance limits. An iterative Support Vector Machine model is integrated with the existing dynamic Parameter Testing Platform (DPTP) for selective (rather than exhaustive) evaluation of dynamic instances to inform on algorithm capability.

关键词： dynamic multi-objective optimization dynamic optimization support vector machine machine learning evolutionary algorithm

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Hybrid response dynamic multi-objective optimization algorithm based on multi-arm bandit model

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INFORMATION SCIENCES 2024年 681卷

作者： Hu, Xiaolin Wu, Lingyu Han, Mingzhang Zhao, Xinchao Sang, Xinzhu Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Key Lab Math & Informat Networks Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

dynamic multi-objective optimization is a relatively challenging problem within the field of multi objective optimization. Nevertheless, these problems have significant real-world applications. The key to addressing dynamic multi-objective problems effectively is promptly tracking changes in the Pareto set (PS) and Pareto front (PF). dynamic multi-objective optimization encompasses various types of problems, and a single-type response strategy proves effective for some specific scenarios. However, as problem complexity and diversity increase, a single-type response strategy often falls short in solving dynamic multi-objective optimization problems. To address this issue, this paper proposes a hybrid response dynamic multi-objective optimization algorithm. The suggested algorithm utilizes the multi-arm bandit model to adaptively adjust the proportion of different response strategies for each type of multi-objective optimization problem. Furthermore, it achieves rapid convergence through an enhanced two-stage MOEA/D. Experiments demonstrate the effectiveness of the strategies employed in the proposed algorithm and its competitiveness compared to other state-of-the-art algorithms.

关键词： dynamic multi-objective optimization Hybrid response strategy multi-arm bandit algorithm Decomposition

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The IGD-based prediction strategy for dynamic multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Hu, Yaru Peng, Jiankang Ou, Junwei Li, Yana Zheng, Jinhua Zou, Juan Jiang, Shouyong Yang, Shengxiang Li, Jun Xiangtan Univ Dept Comp Sci Coll Xiangtan 411105 Peoples R China Xiangtan Univ Cyberspace Secur Coll Xiangtan 411105 Peoples R China Cent South Univ Dept Automat Changsha Peoples R China Montfort Univ Ctr Computat Intelligence Sch Comp Sci & Informat Leicester LE1 9BH England Hunan Inst Engn Xiangtan 411105 Peoples R China

In recent years, an increasing number of prediction-based strategies have shown promising results in handling dynamic multi-objective optimization problems (DMOPs), and prediction models are also considered to be very favorable. Nevertheless, some linear prediction models may not always be effective. In particular, when the motion direction trends of different individuals are not aligned, these models can yield inaccurate prediction results. Inverted generational distance (IGD) is a commonly used metric for evaluating the performance of algorithms. This paper proposes a prediction model based on the IGD metric. Specifically, we assume that the pareto optimal front (POF) of the population at the previous time step is the true POF, and the POF at the current time step is the approximate POF. We cluster the current population with reference to the euclidean distances from uniform points on the true POF to the current POF points, with slight overlap between adjacent clusters, enables a better tradeoff between convergence and diversity in the prediction process. We consider the movement directions of individuals within each cluster separately through different cluster distributions, while balancing the individual movement directions and the overall population movement direction by overlaying cluster coverage areas, thereby helping to avoid the clustered prediction population from getting trapped in local optima. Experimental results and comparisons with other algorithms demonstrate that this strategy exhibits strong competitiveness in handling DMOPs.

关键词： Evolutionary algorithms dynamic multi-objective optimization Prediction strategy Inverted generational distance

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A new framework of change response for dynamic multi-objective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 248卷

作者： Hu, Yaru Zou, Juan Zheng, Jinhua Jiang, Shouyong Yang, Shengxiang Xiangtan Univ Sch Comp Sci & Cyberspace Secur Xiangtan 411105 Peoples R China Cent South Univ Dept Automat Changsha 410083 Peoples R China De Montfort Univ Inst Artificial Intelligence Sch Comp Sci & Informat Leicester LE1 9BH England

Combining response strategies into multi -objective evolutionary algorithms (MOEAs) for dynamic multiobjective optimization problems (DMOPs) is very popular. However, most of them hardly focus on DMOPs via enhancing the operator's searching ability of MOEAs. We present a new framework of change response called MOEA/D-HSS. When a change is detected, MOEA/D-HSS updates and assesses saved historical information, computing the intensity of change on the decision variables and the similarity between the current environment and historical ones. Hybrid search strategies (HSS) adaptively adjust the searching range of the population in each generational cycle based on the knowledge above, which has a great chance of discovering new promising regions. HSS is integrated into the variation operator of MOEA based on decomposition (MOEA/D-DE) to enhance its search ability. We take into account that the historical information may be useless references in the later stage of the evolution. Thus, the frequency of HSS usage is gradually decreased in every time interval to balance the population's convergence and diversity. Experimental results demonstrate that MOEA/S-HSS is very competitive on most benchmark problems compared with other state-of-the-art algorithms.

关键词： Evolutionary algorithms dynamic multi-objective optimization Prediction method Self-learning reference points

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Scalable benchmarks and performance measures for dynamic multi-objective optimization

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APPLIED SOFT COMPUTING 2024年 159卷

作者： Sun, Baiqing Zhang, Changsheng Zhao, Haitong Yu, Zhang Northeastern Univ Dept Software Coll Shenyang 110819 Peoples R China Ningxia Inst Sci & Technol Shizuishan 753000 Ningxia Peoples R China Changshu Inst Technol Sch Comp Sci & Engn Changshu 215500 Peoples R China China Telecom Digital Intelligence Technol Co Ltd Beijing Peoples R China

dynamic multi -objective optimization problems (DMOPs) can be utilized to model certain real -world problems that have a dynamic nature. Algorithms for solving DMOPs can be evaluated and improved by comparing their performance on different benchmarks. However, some existing benchmarks for DMOPs have the limitation of non -uniform weights for decision variables. Additionally, dynamic many -objective optimization problems (DMaOPs) involve more than three objectives, but only a few existing benchmarks can be extended to accommodate DMaOPs. Furthermore, some existing performance measures for DMOPs may not effectively compare the relative performance differences between multiple algorithms or evaluate the search uniformity among different objectives. In this paper, we propose improvements to an existing benchmark for DMOPs by expanding the impact range of decision variables. Moreover, a benchmark framework that can be extended to accommodate DMaOPs is proposed, thus addressing a research gap between the optimization of DMOPs and DMaOPs. Additionally, a set of performance measures for DMOPs are proposed, which can evaluate the relative performance and search uniformity of multi -objective optimization algorithms. By comparing the performance of state-of-the-art and commonly used algorithms on test problems, we can gain a better understanding of the characteristics and strengths and weaknesses of the algorithms and test problems.

关键词： dynamic multi-objective optimization Benchmark Performance measures Scalable test functions

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Guided prediction strategy based on regional multi-directional information fusion for dynamic multi-objective optimization

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INFORMATION SCIENCES 2024年 669卷

作者： Feng, Jinyu Chen, Debao Zou, Feng Ge, Fangzhen Bian, Xiaotong Zhang, Xuenan Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Intelligent Comp & Applicat Key Lab Anhui Huaibei 235000 Anhui Peoples R China Suzhou Univ Sch Informat Engn Suzhou 234000 Peoples R China

Region partitioning is effective for solving dynamic multi-objective optimization problems (DMOPs). However, most region partitioning approaches use only specific individual information to predict directions within each region. Their efficiency degrades when the distribution of individuals is irregular, and the use of several methods to obtain high-quality areas incurs high computational costs. To address these problems, this study develops a guided prediction strategy based on regional multi-directional information fusion for dynamic multi-objective optimization (RMDIF). Firstly, quantiles are used in the subregional segmentation, whose computational cost is small. Secondly, to increase the prediction accuracy and adaptability of the algorithm for individuals with irregular distributions, information from the center and boundary points of each subregion is fused to construct a new direction for generating initial individuals in new environments. Similar to the quantile-guided dual-prediction strategy, a dual-space prediction strategy is used to generate individuals in new environments to increase the population diversity. Finally, a "maintain-decline-maintain" strategy is used to determine the proportion of new individuals from two prediction spaces. Compared with the fixed proportion method, the proposed method better balances convergence and diversity. RMDIF and six other algorithms are tested on 27 DMOPs, the proposed algorithm outperformed the others in most cases.

关键词： dynamic multi-objective optimization Regional multi-directional information Prediction Adaptive adjustment

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