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A framework based on generational and environmental response strategies for dynamic multi-objective optimization

APPLIED SOFT COMPUTING 2024年 152卷

作者： Li, Qingya Liu, Xiangzhi Wang, Fuqiang Wang, Shuai Zhang, Peng Wu, Xiaoming Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain inspired Intelligent Shenzhen 518055 Peoples R China Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Natl Supercomp Ctr JinanShandong Prov Key Lab Com Jinan Shandong Peoples R China Qilu Univ Technol Shandong Acad Sci Biol Engn Technol Innovat Ctr Shandong Prov Heze Branch Jinan Shandong Peoples R China

Due to the dynamics and uncertainty of the dynamic multi-objective optimization problems (DMOPs), it is difficult for algorithms to find a satisfactory solution set before the next environmental change, especially for some complex environments. One reason may be that the information in the environmental static stage cannot be used well in the traditional framework. In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages. Unlike in the traditional framework, response strategies are only run in the environmental change stage. For simplicity, the feed-forward center point strategy was chosen to be the response strategy in the novel dynamic framework (FGERS-CPS). FGERS-CPS is not only to predict change trend of the optimum solution set in the environmental change stage, but to predict the evolution trend of the population after several generations in the environmental static stage. Together with the feed-forward center point strategy, a simple memory strategy and adaptive diversity maintenance strategy were used to form the complete FGERS-CPS. On 13 DMOPs with various characteristics, FGERS-CPS was compared with four classical response strategies in the traditional framework. Experimental results show that FGERS-CPS is effective for DMOPs.

关键词： dynamic multi-objective optimization Novel dynamic framework Center point Genetic algorithm Prediction

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Adaptive dynamic environment response based evolutionary algorithm for dynamic multi-objective optimization 34

Adaptive dynamic environment response based evolutionary alg...

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34th Chinese Control and Decision Conference (CCDC)

作者： Liu, Kanrong Liu, Jianchang Tan, Shubin Li, Fei Zheng, Tianzi Liu, Yuanchao Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Anhui Univ Technol Dept Elect & Informat Engn Maanshan 243032 Anhui Peoples R China Anhui Univ Technol Anhui Prov Key Lab Special Heavy Load Robot Maanshan 243032 Anhui Peoples R China

ISBN: (纸本)9781665478960

In dynamic multi-objective optimization problems (DMOPs), multiple conflicting objectives vary over time. Therefore, a core issue for the DMOPs is to respond the dynamic environments, when the environment change has been detected. To effectively address DMOPs, a novel method, called adaptive dynamic environment response based dynamic multi-objective evolutionary algorithm (ADER-DMOEA), is proposed in this paper. The proposed algorithm can adaptively respond the dynamic environment based on the detected environment change severity. First, a novel environment change severity detection mechanism is developed, where C-metric is introduced to measure the performance of different population obtained by different types of response strategies. Second, according to the measurement results, the algorithm adaptively adopts different strategies for environment response. Therefore, it can ensure that the proposed method has a good initial population for each new environment. The proposed method is compared with three algorithms, and the experiment results have demonstrated that ADER-DMOEA is competitive in dealing with dynamic environments.

关键词： Adaptive dynamic environment response dynamic multi-objective optimization Prediction Memory Response mechanism C-metric

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A novel quantile-guided dual prediction strategies for dynamic multi-objective optimization

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INFORMATION SCIENCES 2021年 579卷 751-775页

作者： Sun, Hao Cao, Anran Hu, Ziyu Li, Xiaxia Zhao, Zhiwei Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligen Qinhuangdao 066004 Hebei Peoples R China Tangshan Univ Dept Comp Sci & Technol Tangshan 063000 Hebei Peoples R China

dynamic multi-objective optimization problems (DMOPs) require evolutionary algorithms (EAs) to accurately track the Pareto-optimal Front (PF) and generate the solutions along the PF in the constantly changing environment. In order to solve the DMOPs, a novel quantileguided dual prediction strategies evolutionary algorithm (NQDPEA) is proposed in this paper. Quantiles are often employed to characterize data in statistics. In NQDPEA, the evolution of the population is guided by the quantile, which is to predict the position of the quantile in a new environment through historical quantile information. Then, a new solution set is expanded according to the location of the new quantile. Moreover, its prediction strategies not only predict Pareto-optimal set (PS) by quantile in the decision space but also predict the PF by quantile in objective space and then mapping back to decision space. Through the adaptive combination strategy, the proportion of the new solutions produced by each prediction strategy changes adaptively. To prove the performance of NQDPEA, it is compared with four powerful EAs on 13 test instances. Experimental results show that NQDPEA can effectively generate high quality solutions uniformly along PF. (c) 2021 Elsevier Inc. All rights reserved.

关键词： dynamic multi-objective optimization Quantile Dual prediction

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A new prediction strategy for dynamic multi-objective optimization using Gaussian Mixture Model

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INFORMATION SCIENCES 2021年 580卷 331-351页

作者： Wang, Feng Liao, Fanshu Li, Yixuan Wang, Hui Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China

dynamic multi-objective optimization problems (DMOPs), in which the environments change over time, have attracted many researchers' attention in recent years. Since the Pareto set (PS) or the Pareto front (PF) can change over time, how to track the movement of the PS or PF is a challenging problem in DMOPs. Over the past few years, lots of methods have been proposed, and the prediction based strategy has been considered the most effective way to track the new PS. However, the performance of most existing prediction strategies depends greatly on the quantity and quality of the historical information and will deteriorate due to non-linear changes, leading to poor results. In this paper, we propose a new prediction method, named MOEA/D-GMM, which incorporates the Gaussian Mixture Model (GMM) into the MOEA/D framework for the prediction of the new PS when changes occur. Since GMM is a powerful non-linear model to accurately fit various data distributions, it can effectively generate solutions with better quality according to the distributions. In the proposed algorithm, a change type detection strategy is first designed to estimate an approximate PS according to different change types. Then, GMM is employed to make a more accurate prediction by training it with the approximate PS. To overcome the shortcoming of a lack of training solutions for GMM, the Empirical Cumulative Distribution Function (ECDF) method is used to resample more training solutions before GMM training. Experimental results on various benchmark test problems and a classical real-world problem show that, compared with some state-of-the-art dynamic optimization algorithms, MOEA/D-GMM outperforms others in most cases. (c) 2021 Elsevier Inc. All rights reserved.

关键词： dynamic multi-objective optimization Gaussian Mixture Model Change type detection Resampling

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Feature information prediction algorithm for dynamic multi-objective optimization problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期295卷 965-981页

作者： Ma, Xuemin Yang, Jingming Sun, Hao Hu, Ziyu Wei, Lixin Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

dynamic multi-objective optimization problems (DMOPs) contain multiple conflicting goals while track -ing the changing Pareto-optimal front (PF) or Pareto-optimal set (PS). Most algorithms treat the solutions of DMOPs as if they were dealing with static multi-objective optimization problems. However, solutions under different environments may obey different distributions. To solve some of the existing limitations of currently available methods, a dynamic multi-objective optimization algorithm based on feature in-formation prediction (FIP) is proposed. To identify the distribution of solutions after an environmental change, joint distribution adaptation (JDA) is used to construct a mapping function. The feature informa-tion, which is extracted from the objective space at the current time step, is mapped to a higher dimen-sional space. Then the feature information of decision space at the next time step is obtained using the interior point method. Based on this information, the initial population at the next time step is gener-ated when a change is detected. The performance of FIP is validated by comparing it with respect to four state-of-the-art evolutionary algorithms on eight benchmark functions. Experimental results demonstrate that FIP can quickly cover the front with rapidly changing environments. (c) 2021 Published by Elsevier B.V.

关键词： Evolutionary computations dynamic multi-objective optimization Different distributions Feature information prediction Joint distribution adaptation

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A reinforcement learning approach for dynamic multi-objective optimization

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INFORMATION SCIENCES 2021年 546卷 815-834页

作者： Zou, Fei Yen, Gary G. Tang, Lixin Wang, Chunfeng Northeastern Univ Key Lab Data Analyt & Optimizat Smart Ind Liaoning Engn Lab Operat Analyt & Optimizat Smart Minist Educ Shenyang 110819 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Northeastern Univ Liaoning Key Lab Mfg Syst & Logist Inst Ind & Syst Engn Shenyang 110819 Peoples R China Henan Normal Univ Coll Math & Informat Sci Xinxiang 453007 Henan Peoples R China

dynamic multi-objective optimization Problem (DMOP) is emerging in recent years as a major real-world optimization problem receiving considerable attention. Tracking the movement of Pareto front efficiently and effectively over time has been a central issue in solving DMOPs. In this paper, a reinforcement learning-based dynamic multi-objective evolutionary algorithm, called RL-DMOEA, which seamlessly integrates reinforcement learning framework and three change response mechanisms, is proposed for solving DMOPs. The proposed algorithm relocates the individuals based on the severity degree of environmental changes, which is estimated through the corresponding changes in the objective space of their decision variables. When identifying different severity degree of environmental changes, the proposed RL-DMOEA approach can learn better evolutionary behaviors from environment information, based on which apply the appropriate response mechanisms. Specifically, these change response mechanisms including the knee-based prediction, center-based prediction and indicator-based local search, are devised to promote both convergence and diversity of the algorithm under different severity of environmental changes. To verify this idea, the proposed RL-DMOEA is evaluated on CEC 2015 test problems involving various problem characteristics. Empirical studies on chosen state-ofthe-art designs validate that the proposed RL-DMOEA is effective in addressing the DMOPs. (c) 2020 Elsevier Inc. All rights reserved.

关键词： dynamic multi-objective optimization Severity degree Reinforcement learning Prediction

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Performance metrics analysis of dynamic multi-objective optimization for energy consumption and productivity improvement in batch electrodialysis

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CHEMICAL ENGINEERING COMMUNICATIONS 2021年第4期208卷 517-529页

作者： Rohman, Fakhrony Sholahudin Aziz, Norashid Univ Sains Malaysia Sch Chem Engn Engn Campus Seri Ampangan Penang Malaysia

Empty fruit bunch (EFB) can be utilized to produce sugar via hydrochloric acid (HCl) hydrolysis where the HCl must be concentrated to retain the pure sugar. Electrodialysis (ED) is an effective method for acid-sugar separation and has to be optimized to ensure that the most economical operation can be achieved. However, ED batch optimization consists of conflicting multiple objectives that are required to be solved as a dynamic multi-objective optimization (MOO) problem. In the past, the dynamic optimization of acid-sugar ED separation was carried out by solving a single objective optimization problem in sequence but this approach could not be accomplished with different objective functions simultaneously. In this work, the results of the MOO which consist of a number of optimal solutions are configured as a Pareto Front (PF). The weighted sum, the ?-constraint, and the elitist non-dominated sorting genetic algorithm or NSGA-II approaches are applied to solve the conflicting objectives functions i.e. minimization of energy consumption and maximization of acid concentration These result in a set of several Pareto optimal solutions with two objectives: maximizing concentrated acid ranging from 1.89?M to 2.1?M is accompanied by minimizing energy consumption ranging from 630?Wh to 848?Wh. Each point of the Pareto solutions contains a different combination of optimal current density and flowrate trajectories which promotes a different amount of energy consumption and acid concentration. This set of solutions provides choices in assessing the tradeoffs and determining the most appropriate operating policy.

关键词： Electrodialysis Energy consumption dynamic multi-objective optimization Optimal control trajectory

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Elitism-based transfer learning and diversity maintenance for dynamic multi-objective optimization

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INFORMATION SCIENCES 2023年第1期636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 Surrey England

In handling dynamic multi-objective optimization problems (DMOPs), transfer learning driven methods have received considerable attention for finding a high-quality initial population with good convergence and diversity performance to adapt to the new environment. However, they commonly suffer from loss of population diversity and high computational consumption. Therefore, this study proposes a hybrid method combining elitism-based transfer learning and diversity maintenance to efficiently identify a high-quality initial population in response to environmental changes. An elite selection mechanism is developed to select elite individuals from the memory pool when the environment changes. Subsequently, an elitism-based transfer learning method is proposed to predict individuals by leveraging the knowledge from the selected elite individuals, thereby improving the computational efficiency and quality of the solutions. Subsequently, a random diversity maintenance strategy is developed to generate diverse individuals within the regions where the predicted individuals are located to defy the loss of diversity in the population. Finally, the generated diverse and predicted individuals are merged to form an initial population to adapt to the new environment. The experimental results have demonstrated the competitiveness of the proposed algorithm for most DMOP test instances in terms of convergence, diversity, and computational efficiency.

关键词： dynamic multi-objective optimization Transfer learning Evolutionary algorithm Diversity maintenance

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An immune inspired multi-agent system for dynamic multi-objective optimization

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KNOWLEDGE-BASED SYSTEMS 2023年第1期262卷

作者： Kamali, Seyed Ruhollah Banirostam, Touraj Motameni, Homayun Teshnehlab, Mohammad Islamic Azad Univ Dept Comp Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran K N Toosi Univ Technol Elect & Comp Engn Tehran Iran

In this research, an immune inspired multi-agent system (IMAS) is proposed to solve optimization problems in dynamic and multi-objective environments. The proposed IMAS uses artificial immune system metaphors to shape the local behaviors of agents to detect environmental changes, generate Pareto optimal solutions, and react to the dynamics of the problem environment. Apart from that, agents enhance their adaptive capacity in dealing with environmental changes to find the global optimum, with a hierarchical structure without any central control. This study used a combination of diversity-, multi-population-and memory-based approaches to perform better in multi-objective environments with severe and frequent changes. The proposed IMAS is compared with six state-of-the-art algorithms on various benchmark problems. The results indicate its superiority in many of the experiments.(c) 2022 Published by Elsevier B.V.

关键词： Immune inspired multi-agent system dynamic multi-objective optimization Severe and frequent changes

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A dynamic multi-objective optimization based on a hybrid of pivot points prediction and diversity strategies

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 78卷

作者： Zheng, Jinhua Zhou, Fei Zou, Juan Yang, Shengxiang Hu, Yaru Xiangtan Univ Sch Comp Sci Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan Hunan Peoples R China Xiangtan Univ Sch Cyberspace Sci Xiangtan Hunan Peoples R China Xiangtan Univ Fac Sch Comp Sci Xiangtan 411105 Peoples R China Xiangtan Univ Sch Cyberspace Sci Xiangtan 411105 Peoples R China Xiangtan Univ Dept Math & Computat Sci Xiangtan 411105 Peoples R China Hunan Prov Key Lab Intelligent Informat Proc & App Hengyang 421002 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England

There are many dynamic multi-objective optimization problems (DMOPs) in real-world applications. The Pareto-optimal front (PF) or Pareto-optimal set (PS) of such problems will change with the optimization process, posing a great challenge to the evolutionary algorithms(EAs). The prediction-based strategy is a commonly used strategy for DMOPs. However, the prediction-based strategy can only solve predictable problems. Additionally, using the prediction-based strategy alone may increase the imbalance of population convergence and diversity. Based on the above issues the prediction-based strategy faces, we believe that the lack of population diversity causes them. Therefore, this paper proposes a novel dynamic optimization strategy, including the prediction and diversity strategy (HPPDS) for DMOPs. First, the prediction strategies combine the prediction of pivot points (representative individuals selected through a dynamic selection mechanism) and non-dominant individuals to achieve the ability to solve predictable DMOPs. Then, a specific and controllable diversity strategy is used to maintain the population's diversity and deal with the unpredictable DMOPs. This way, our proposed optimization algorithm can cope with various environmental changes in DMOPs. To verify that our proposed strategy is competitive, HPPDS is compared with five other state-of-art strategies. The experimental results show that the HPPDS is competitive.

关键词： dynamic multi-objective optimization dynamic selection mechanism Pivot points

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