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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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Cooperative Co-evolutionary Algorithm for dynamic multi-objective optimization Based on Environmental Variable Grouping

Cooperative Co-evolutionary Algorithm for Dynamic Multi-obje...

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7th International Conference on Swarm Intelligence (ICSI)

作者： Xu, Biao Zhang, Yong Gong, Dunwei Rong, Miao China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Peoples R China Huaibei Normal Univ Sch Math & Sci Huaibei 235000 Peoples R China

ISBN: (纸本)9783319410005;9783319409993

This paper presents a cooperative co-evolutionary dynamic multi-objective optimization algorithm, i.e., DNSGAII-CO for solving DMOPs based on environmental variable grouping. In this algorithm, a new method of grouping decision variables is first presented, in which all the decision variables are divided into two subcomponents according to whether they are interrelated with or without environment parameters. Then, when cooperatively optimizing the two subcomponents by using two populations, two prediction methods, i.e., differential prediction and Cauchy mutation, are employed to initialize them, respectively. The proposed algorithm is applied to a benchmark DMOPs, and compared with two state-of-the-art algorithms. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of convergence and distribution.

关键词： dynamic multi-objective optimization Cooperative co-evolution Decision variable grouping

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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 novel predictive method based on key points for dynamic multi-objective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期190卷 116127-116127页

作者： Wang, Chunfeng Yen, Gary G. Zou, Fei Xianyang Normal Univ Sch Math & Stat Xianyang 712000 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China

dynamic multi-objective problem is very difficult to be solved because of the variability of the objective function with time. To overcome the difficult caused by such variability, a predictive method utilizing some key points (including polar points and centroid points) is designed, which contains four critical steps. First, the whole population is automatically divided into multiple clusters, which will be used to preserve a good diversity in the process of population evolution. Second, the technique for order of preference by similarity to ideal solution (TOPSIS), a well-regarded multi-attribute decision making strategy, is exploited to improve its convergence speed further. Third, the polar point and centroid point in each cluster are utilized to obtain the initial population by using sequence predictive method when environmental changes are detected. Fourth, to accelerate the convergence speed, the quantitative value for each individual determined in the prediction process is also used in mating selection and environmental selection. The numerical results imply that the new method can deal with the change of environment effectively and track the Pareto optimal front (POF) quickly. Meanwhile, the comparison results with several selected state-of-the-art methods also show that the overall performance of the proposed method is the best on most benchmark problems.

关键词： dynamic multi-objective optimization TOPSIS Clustering strategy Predictive strategy

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A dynamic multi-objective optimization evolutionary algorithm based on particle swarm prediction strategy and prediction adjustment strategy

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SWARM AND EVOLUTIONARY COMPUTATION 2022年第0期75卷

作者： Wang, Peidi Ma, Yongjie Wang, Minghao Northwest Normal Univ Sch Phys & Elect Engn Lanzhou 730070 Gansu Peoples R China

dynamic multi-objective optimization Evolutionary Algorithm (DMOEA) is a promising approach for solving dynamic multi-objective optimization Problems (DMOPs), which has attracted extensive attention owing to its wide application value recently. The focus and difficulty of DMOEA research is to design algorithms that can quickly respond to environmental changes. To achieve this goal, a novel DMOEA based on particle swarm prediction strategy and prediction adjustment strategy is proposed in this paper. After detecting environmental changes, the particle swarm prediction strategy is implemented through the population information at the moment before environmental change to form a predicted population that approximates the new Pareto optimal set (PS)/Pareto optimal front (PF) as much as possible. In addition, by comparing the individual information of the predicted population with that of the population at the moment before the environmental change, a prediction adjustment strategy is given to leave the better individuals to form the initial population in the new environment. The performance of the algorithm is evaluated on the DF test cases and compared with the advanced DMOEAs. The results show that the algorithm has competitive performance in most of the test problems.

关键词： dynamic multi-objective optimization Evolutionary Algorithm Particle swarm prediction strategy Prediction adjustment strategy

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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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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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A dynamic multi-objective optimization evolutionary algorithm for complex environmental changes

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KNOWLEDGE-BASED SYSTEMS 2021年 216卷 106612-106612页

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

dynamic multi-objective optimization problems (DMOPs) have attracted more and more research in the field of evolutionary computation community in recent years. Unlike most existing approaches just for solving a single change type, we propose a novel dynamic diversity introduction strategy (DDIS), which aims to solve DMOPs with mixed complex environmental changes. Two types of change intensity are presented to jointly determine the proportion of diversity introduction and whether the change type is drastic or slow, and then the inverse modeling and partial population random initialization are served as diversity introduction strategies to respond to environmental changes respectively. The proposed DDIS is incorporated into the multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework, called DDIS-MOEA/D. For verifying the performance of DDIS, three different mixed change types are constructed by varying severity or frequency of changes and then the proposed algorithm is tested on GTA benchmark problems under the three dynamic characteristics. Experimental results confirm that the proposed approach can successfully identify different change types and dynamically track and adapt complex environmental changes. (C) 2020 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization dynamic diversity introduction strategy Inverse modeling Change intensity Complex environmental changes

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Enhancing dynamic multi-objective optimization Using Opposition-based Learning and Simulated Annealing

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2023年第4期32卷 2350037-2350037页

作者： Ilyas, Kiran Younas, Irfan Univ Management & Technol Sch Syst & Technol Lahore 54000 Pakistan Natl Univ Comp & Emerging Sci FAST Sch Comp Lahore 54000 Pakistan

There are many dynamic real-life optimization problems in which objectives increase or decrease over time, which usually leads to variations in the dimensions of a Pareto front. dynamic multi-objective optimization (DyMO) approaches aim to keep track of the updated Pareto front to tackle the changes which are caused by the dynamic environment. However, the current DyMO approaches do not handle dynamic environments effectively. In this study, a new hybrid dynamic two-archive evolutionary algorithm with a newly added simulated annealing and opposition-based learning strategy is proposed. The proposed method helps to preserve solutions with reasonable diversity and improve convergence by searching for promising solutions within acceptable computational time and effort. To evaluate the efficacy of the suggested method, comprehensive experiments using different multi-objective quality measures such as generational distance, and inverted generational distance have been performed on several benchmark problems with varying numbers of objectives over time. The results of the experiments show that the suggested method outperforms the strategies already in use.

关键词： dynamic multi-objective optimization optimization opposition-based learning simulated annealing

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