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A random benchmark suite and a new reaction strategy in dynamic multiobjective optimization

SWARM AND EVOLUTIONARY COMPUTATION 2021年 63卷 100867-100867页

作者： Ruan, Gan Zheng, Jinhua Zou, Juan Ma, Zhongwei Yang, Shengxiang Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Xiangtan Univ Informat Engn Coll Xiangtan Hunan Peoples R China Hengyang Normal Univ Minist Educ Hengyang Hunan Peoples R China Hengyang Normal Univ Sch Comp Sci & Technol Hengyang Hunan Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

In the domain of evolutionary computation, more and more attention has been paid to dynamic multiobjective optimization. Generally, artificial benchmarks are effective tools for the performance evaluation of dynamic multiobjective evolutionary algorithms (DMOEAs). After reviewing existing benchmarks and highlighting their weaknesses, this paper proposes a new benchmark suite to promote the comprehensive testing of algorithms. This proposed benchmark suite has eight random instances in which the randomness is produced by designed random time sequences. Also, this suite introduces challenging but rarely considered characteristics, including diverse features in fitness landscape (e.g. deception, multimodality, and bias) and complex trade-off geometries (e.g. convexity-concavity mixed geometry and disconnected geometry). Empirical studies have shown that the proposed benchmark poses reasonable challenges to DMOEAs in terms of convergence and diversity. Besides, a center matching strategy (CMS) is suggested to track random changes in these problems, which applies the history individual information in a global scope for population prediction. Compared with other reaction strategies, CMS has been demonstrated to be very competitive in dealing with random problems.

关键词： Evolutionary algorithms dynamic multiobjective optimization Random test problems Center matching strategy

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Manifold clustering-based prediction for dynamic multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 77卷

作者： Yan, Li Qi, Wenlong Qin, A. K. Yang, Shengxiang Gong, Dunwei Qu, Boyang Liang, Jing Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic 3122 Australia De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China

Prediction-based evolutionary algorithms have gained much attention in solving dynamic multiobjective opti-mization problems due to their impressive performance in tracking the changing Pareto set (PS). Current ap-proaches focus on developing learning or estimation models to reveal the dynamic regularities from the correlations between the historical PSs. However, the underlying knowledge in the PS itself, such as the neighborhood distribution of the individuals and their local correlation in the decision space, is ignored which may affect prediction accuracy and the quality of the predicted population. Therefore, a manifold clustering -based predictor is proposed in this paper. A manifold learning method is introduced to preprocess the histori-cal PSs to find and reserve the intrinsic neighborhood relationship of the individuals. As a result, a number of local linear manifolds are extracted from each historical PS, and the individuals in a population are divided into several clusters according to the different linear manifolds they attach to. The individuals belonging to one cluster can be regarded as linearly correlated and may have a similar moving trend. Thus, the subsequent pre-diction is conducted in units of the cluster and multiple prediction models are built to predict the new PS in a decomposition manner. Finally, an initial population with good diversity and distribution can be generated for the new environment. The proposed algorithm is tested on a variety of commonly-used benchmark problems and compared with eight state-of-the-art algorithms. Experimental results confirm the efficacy of the proposed al-gorithm, especially on the problems with nonlinear correlation between the decision variables.

关键词： dynamic multiobjective optimization Evolutionary algorithm Manifold clustering Prediction

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Quantile-guided multi-strategy algorithm for dynamic multiobjective optimization

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APPLIED SOFT COMPUTING 2023年 133卷

作者： Cao, Anran Sun, Hao Hu, Ziyu Chen, Pengfei Zhao, Zhiwei Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligen Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China Tangshan Univ Dept Comp Sci & Technol Tangshan 063000 Hebei Peoples R China

dynamic multiobjective optimization problems (DMOPs) require that the algorithm is capable of tracking the position of Pareto-optimal solution (PS) in a rapidly changing environment. Prediction -based and memory-based methods currently gain much attention for their good tracking ability. However, an elaborate predictor may not be suitable for different problems and the improper reuse of the memory may lower the quality of solution. To overcome these limitations, a quantile-guided multi -strategy algorithm (QMA) for dynamic multiobjective optimization is proposed in this paper. Quantile is always employed to represent characteristics of data because of its robustness to the outliers. In QMA, historical quantile information of decision space is used to find the new position of quantile. Then, the solution set is expanded based on the new quantile. Moreover, historical search information is used to assist in the evolution of population, which is achieved through selecting the mapping matrix as a judgment of retrieval. Meanwhile, the quantile-guided environmental change degree detector is employed to determine the number of retained and the randomly generated individuals when there is no similarity. Experimental results carried out on various DMOPs demonstrate that QMA is highly competitive compared with some state-of-the-art algorithms. (c) 2022 Elsevier B.V. All rights reserved.

关键词： dynamic multiobjective optimization Quantile Prediction Mapping matrix

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A grey prediction-based evolutionary algorithm for dynamic multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2020年 56卷 100695-100695页

作者： Wang, Chunfeng Yen, Gary G. Jiang, Min Henan Normal Univ Coll Math & Informat Sci Xinxiang 453007 Henan Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Xiamen Univ Sch Informat Xiamen 361005 Fujian Peoples R China

dynamic multiobjective optimization problems (DMOPs) usually involve multiple conflicting objectives that change over time. A good evolutionary algorithm should be able to quickly track the moving Pareto optimal front (POF) and Pareto optimal set (POS) over time. To solve DMOPs, a predictive method is proposed herein based on grey prediction model, which is composed of three essential ingredients. The first one is that the population is divided into multiple clusters, which can help the population to preserve diversity throughout the evolutionary process. The second one is that the individuals used to detect environmental changes are taken from different clusters, which in turn help the proposed algorithm to detect environmental changes more promptly and accurately. The third one is to build the grey prediction model by using the centroid point of each cluster when detecting the environmental change, and then generate the initial population. Empirical results show that the proposed algorithm can deal with dynamic environments and track the varying POS and POF effectively and efficiently, and achieve better performances on most test problems than several selected state-of-the-art algorithms.

关键词： dynamic multiobjective optimization Memory-based strategy Predictive strategy Grey model

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dynamic multiobjective Evolutionary optimization via Knowledge Transfer and Maintenance

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第2期54卷 936-949页

作者： Lin, Qiuzhen Ye, Yulong Ma, Lijia 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

This article suggests a new dynamic multiobjective evolutionary algorithm (DMOEA) with Knowledge Transfer and Maintenance, called KTM-DMOEA, which aims to alleviate the negative transfer and enhance the optimization efficiency. Two strategies, i.e., knowledge transfer prediction (KTP) and knowledge maintenance sampling (KMS), are proposed to excavate useful knowledge from historical environments. Particularly, KTP is a discriminative predictor designed to reduce the feature and distribution divergences across distinct environments, which classifies high-quality solutions from a large number of randomly generated solutions in new environment. Moreover, KMS is a generative predictor by modeling the distribution of elitist solutions in last environment, which can sample superior solutions in new environment according to the dynamic change trends. In this way, the advantages of KTP and KMS strategies are combined to produce a superior initial population in new environment, which help to alleviate the negative transfer and resultantly enhance the overall performance of KTM-DMOEA. When compared to several recently reported DMOEAs, the experimental results validate the advantages of KTM-DMOEA in tackling most cases of benchmark and real-world problems.

关键词： optimization Maintenance engineering Heuristic algorithms Sociology Knowledge transfer Diversity reception Transfer learning dynamic multiobjective optimization evolutionary algorithm knowledge maintenance knowledge transfer

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A Benchmark Test Suite for dynamic Evolutionary multiobjective optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2017年第2期47卷 461-472页

作者： Gee, Sen Bong Tan, Kay Chen Abbass, Hussein A. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore Univ New South Wales Sch Engn & Informat Technol Campbell ACT 2600 Australia

Growing trend of the dynamic multiobjective optimization research in the evolutionary computation community has increased the need for challenging and conceptually simple benchmark test suite to assess the optimization performance of an algorithm. This paper proposes a new dynamic multiobjective benchmark test suite which contains a number of component functions with clearly defined properties to assess the diversity maintenance and tracking ability of a dynamic multiobjective evolutionary algorithm (MOEA). Time-varying fitness landscape modality, tradeoff connectedness, and tradeoff degeneracy are considered as these properties rarely exist in the current benchmark test instances. Cross-problem comparative study is presented to analyze the sensitivity of a given algorithm to certain fitness landscape properties. To demonstrate the use of the proposed benchmark test suite, three evolutionary multiobjective algorithms, namely nondominated sorting genetic algorithm, decomposition-based MOEA, and recently proposed Kalman-filter-based prediction approach, are analyzed and compared. Besides, two problem-specific performance metrics are designed to assess the convergence and diversity performances, respectively. By applying the proposed test suite and performance metrics, microscopic performance details of these algorithms are uncovered to provide insightful guidance to the algorithm designer.

关键词： Benchmark test suite dynamic multiobjective optimization evolutionary algorithm

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A Multimodel Prediction Method for dynamic multiobjective Evolutionary optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2020年第2期24卷 290-304页

作者： Rong, Miao Gong, Dunwei Pedrycz, Witold Wang, Ling China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 1H9 Canada Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

A large number of prediction strategies are specific to a dynamic multiobjective optimization problem (DMOP) with only one type of the Pareto set (PS) change. However, a continuous DMOP with more than one type of the unknown PS change has been seldom investigated. We present a multimodel prediction approach (MMP) realized in the framework of evolutionary algorithms (EAs) to tackle the problem. In this paper, we first detect the type of the PS change, followed by the selection of an appropriate prediction model to provide an initial population for the subsequent evolution. To observe the influence of MMP on EAs, optimal solutions obtained by three classical dynamic multiobjective EAs with and without MMP are investigated. Furthermore, to investigate the performance of MMP, three state-of-the-art prediction strategies are compared on a large number of dynamic test instances under the same particle swarm optimizer. The experimental results demonstrate that the proposed approach outperforms its counterparts under comparison on most optimization problems.

关键词： Sociology Statistics optimization Predictive models Maintenance engineering Mathematical model Convergence dynamic multiobjective optimization evolutionary algorithm (EA) multimodel prediction particle swarm optimizer type of the Pareto set (PS) change

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A Self-Adaptive Response Strategy for dynamic multiobjective Evolutionary optimization Based on Objective Space Decomposition

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EVOLUTIONARY COMPUTATION 2021年第4期29卷 491-519页

作者： Liu, Ruochen Li, Jianxia Jin, Yaochu Jiao, Licheng Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

dynamic multiobjective optimization deals with simultaneous optimization of multiple conflicting objectives that change over time. Several response strategies for dynamic optimization have been proposed, which do not work well for all types of environmental changes. In this article, we propose a new dynamic multiobjective evolutionary algorithm based on objective space decomposition, in which the maxi-min fitness function is adopted for selection and a self-adaptive response strategy integrating a number of different response strategies is designed to handle unknown environmental changes. The self-adaptive response strategy can adaptively select one of the strategies according to their contributions to the tracking performance in the previous environments. Experimental results indicate that the proposed algorithm is competitive and promising for solving different DMOPs in the presence of unknown environmental changes. Meanwhile, the proposed algorithm is applied to solve the parameter tuning problem of a proportional integral derivative (PID) controller of a dynamic system, obtaining better control effect.

关键词： dynamic multiobjective optimization objective space decomposition maxi-min fitness function self-adaptive response strategy PID control

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Generalized Immigration Schemes for dynamic Evolutionary multiobjective optimization

Generalized Immigration Schemes for Dynamic Evolutionary Mul...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Azevedo, Carlos R. B. Araujo, Aluizio F. R. Univ Estadual Campinas Sch Elect & Comp Engn Campinas SP Brazil Univ Fed Pernambuco Ctr Informat Pernambuco Brazil

ISBN: (纸本)9781424478354

The insertion of atypical solutions (immigrants) in Evolutionary Algorithms populations is a well studied and successful strategy to cope with the difficulties of tracking optima in dynamic environments in single-objective optimization. This paper studies a probabilistic model, suggesting that centroid-based diversity measures can mislead the search towards optima, and presents an extended taxonomy of immigration schemes, from which three immigrants strategies are generalized and integrated into NSGA2 for dynamic multiobjective optimization (DMO). The correlation between two diversity indicators and hypervolume is analyzed in order to assess the influence of the diversity generated by the immigration schemes in the evolution of non-dominated solutions sets on distinct continuous DMO problems under different levels of severity and periodicity of change. Furthermore, the proposed immigration schemes are ranked in terms of the observed offline hypervolume indicator.

关键词： Diversity generation immigrants schemes dynamic multiobjective optimization evolutionary computation

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multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control

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APPLIED SOFT COMPUTING 2008年第2期8卷 959-971页

作者： Zhang, Zhuhong Guizhou Univ Coll Sci Inst Syst Sci & Informat Technol Guiyang 550025 Peoples R China

A novel multiobjective optimization immune algorithm in dynamic environments, as associated with Pareto optimality and immune metaphors of germinal center in the immune system, is proposed to deal with a class of dynamic multiobjective optimization problems which the dimension of the objective space may change over time. Several immune operators, depending on both somatic maturation and T-cell regulation, are designed to adapt to the changing environment so that the algorithm can achieve a reasonable tradeoff between convergence and diversity of population, among which an environmental recognition rule related to the past environmental information is established to identify an appearing environment. Preliminary experiments show that the proposed algorithm cannot only obtain great superiority over two popular algorithms, but also continually track the time-varying environment. Comparative analysis and practical application illustrate its potential. (c) 2007 Elsevier B.V. All rights reserved.

关键词： dynamic multiobjective optimization germinal center immune algorithm Pareto optimality greenhouse control

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