Article
Computer Science, Artificial Intelligence
Wei Zhou, Liang Feng, Kay Chen Tan, Min Jiang, Yong Liu
Summary: Dynamic multiobjective optimization problem refers to a multiobjective optimization problem that varies over time. To solve this kind of problem, evolutionary search with prediction approaches have been developed to estimate the changes in the problem. However, existing prediction methods only focus on the change in the decision space. In this article, a new approach is proposed that conducts prediction from both the decision and objective spaces. Experimental results show the effectiveness of the proposed method in solving both benchmark and real-world DMOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Peidi Wang, Yongjie Ma
Summary: The DMOEA is a powerful solver for DMOPs, but the current algorithms lack strategies in both the environment response and static optimization stages. To address this, a new algorithm was proposed that incorporates different strategies in both stages to balance convergence and diversity. The algorithm uses nondominated solutions-guided evolution in the static optimization stage and fine prediction strategy in the environment response stage to improve performance in dynamic environments.
APPLIED INTELLIGENCE
(2023)
Article
Automation & Control Systems
Xiaxia Li, Jingming Yang, Hao Sun, Ziyu Hu, Anran Cao
Summary: This study introduces a dual prediction strategy with inverse model (DPIM) to enhance the performance of multiobjective optimization problems in dynamic environments. Experimental results demonstrate that DPIM can achieve high-quality populations with good convergence and distribution in dynamic environments.
Article
Automation & Control Systems
Zhengping Liang, Tiancheng Wu, Xiaoliang Ma, Zexuan Zhu, Shengxiang Yang
Summary: In recent years, dynamic multiobjective optimization problems (DMOPs) have gained increasing attention. This article proposes a dynamic multiobjective evolutionary algorithm (DMOEA-DVC) based on decision variable classification, aiming to balance population diversity and convergence. Experimental results comparing DMOEA-DVC with six other algorithms on 33 benchmark DMOPs demonstrate its superior overall performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Ying Chen, Juan Zou, Yuan Liu, Shengxiang Yang, Jinhua Zheng, Weixiong Huang
Summary: This paper proposes a new change response mechanism for dynamic multiobjective optimization problems (DMOPs) by combining a hybrid prediction strategy and a precision controllable mutation strategy. The hybrid prediction strategy enables quick adaptation to predictable environmental changes, while the precision controllable mutation strategy handles unpredictable environmental changes. The mechanism can adapt to various environmental changes and has been shown to be effective and competitive in experimental comparisons.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Engineering, Multidisciplinary
Noha Hamza, Ruhul Sarker, Daryl Essam, Saber Elsayed
Summary: The number of research works on dynamic constrained optimization problems has been increasing rapidly over the past two decades. However, no research on dynamic problems with changes in the coefficients of the constraint functions has been reported. In this paper, a new evolutionary framework with multiple novel mechanisms is proposed to deal with such problems, and the results demonstrate its significant contribution in achieving good quality solutions, high feasibility rates, and fast convergence in rapidly changing environments.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Information Systems
Yingjie Zou, Yuan Liu, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: Sparse large scale multiobjective optimization problems (sparse LSMOPs) have a high degree of sparsity in the decision variables of their Pareto optimal solutions. Existing evolutionary algorithms for sparse LSMOPs fail to achieve sufficient sparsity due to inaccurate location of nonzero decision variables and lack of interaction between the locating process and optimizing process. To address this, a dynamic sparse grouping evolutionary algorithm (DSGEA) is proposed, which groups decision variables with comparable numbers of nonzero variables and applies improved evolutionary operators for optimization. DSGEA outperforms current EAs in experiments on real-world and benchmark problems, achieving sparser Pareto optimal solutions with precise locations of nonzero decision variables.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Xuemin Ma, Jingming Yang, Hao Sun, Ziyu Hu, Lixin Wei
Summary: This paper introduces a multiregional co-evolutionary dynamic multiobjective optimization algorithm, which effectively addresses the dynamic multiobjective optimization problems through a combination of multiregional prediction strategy and multiregional diversity maintenance mechanism, achieving good performance in experiments.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Hui Wang, Zichen Wei, Gan Yu, Shuai Wang, Jiali Wu, Jiawen Liu
Summary: This paper proposes a two-stage many-objective evolutionary algorithm, TS-DGPD, which accelerates convergence and maintains population diversity by using cosine distance and Lp norm, and increases selection pressure using dynamic generalized Pareto dominance. Experimental results show that the algorithm performs well in terms of convergence and diversity.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Engineering, Mechanical
Qiang He, Zheng Xiang, Peng Ren
Summary: In recent years, the dynamic multiobjective optimization problems have attracted great attention, and reusing experiences to establish prediction models has proven to be useful. However, existing methods overlook the importance of environmental selection. This research proposes a new algorithm based on environmental selection and transfer learning to effectively deal with dynamic multiobjective optimization problems.
NONLINEAR DYNAMICS
(2022)
Article
Automation & Control Systems
Min Jiang, Zhenzhong Wang, Liming Qiu, Shihui Guo, Xing Gao, Kay Chen Tan
Summary: A new memory-driven manifold transfer learning-based evolutionary algorithm for dynamic multiobjective optimization (MMTL-DMOEA) is proposed in this article. By combining the mechanism of memory to preserve the best individuals from the past with the feature of manifold transfer learning to predict the optimal individuals, the algorithm significantly improves the quality of solutions at the initial stage and reduces the computational cost required in existing methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Qiuzhen Lin, Yulong Ye, Lijia Ma, Min Jiang, Kay Chen Tan
Summary: This article introduces a new dynamic multiobjective evolutionary algorithm (DMOEA), called KTM-DMOEA, with Knowledge Transfer and Maintenance, which aims to alleviate negative transfer and enhance optimization efficiency. Two strategies, namely knowledge transfer prediction (KTP) and knowledge maintenance sampling (KMS), are proposed to extract useful knowledge and generate a superior initial population, resulting in improved performance.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Qishuang Wu, Juan Zou, Shengxiang Yang, Yaru Hu
Summary: Responding quickly to environmental changes is crucial in solving dynamic multi-objective optimization problems (DMOPs). Most existing methods perform well on predicting individuals but struggle with improving the accuracy of the predicted population. This paper proposes an approach called RVCP, which combines an adjusted reference vector with a multi-objective evolutionary algorithm to predict the population and effectively tackle DMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Ke Shang, Hisao Ishibuchi, Linjun He, Lie Meng Pang
Summary: This article provides a comprehensive survey on the hypervolume indicator widely used in the field of evolutionary multiobjective optimization. The goal is to help researchers deepen their understanding of the principles and applications of the hypervolume indicator, and to promote further utilization of it.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Yuan Yuan, Wolfgang Banzhaf
Summary: We propose a new surrogate-assisted evolutionary algorithm for expensive multiobjective optimization. The algorithm uses two classification-based surrogate models, addresses dominance prediction problem using deep learning techniques, and integrates the surrogate models with multiobjective evolutionary optimization using a two-stage preselection strategy. Experimental results show the superiority of the proposed algorithm compared with several representative surrogate-assisted algorithms.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Zeyu Zhang, Juan Zou, Shengxiang Yang, Junwei Ou, Yaru Hu
Summary: This paper proposes a dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution (ADNEPSO). The algorithm utilizes the complementary characteristics in the search area of the adversarial vector and introduces a novel particle update strategy to enhance performance and adaptability to environmental changes.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Huanrong Tang, Fan Yu, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: The difficulty of solving constrained multi-objective optimization problems lies in balancing constraint satisfaction and objective optimization while considering the diversity of the solution set. In this study, a population state detection strategy and a restart scheme are proposed to address these issues. Experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art constrained multi-objective algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Energy & Fuels
Jiangyang Liu, Xu Yang, Zhongbing Liu, Juan Zou, Yaling Wu, Ling Zhang, Yelin Zhang, Hui Xiao
Summary: This paper proposes an energy utilization system for buildings in hot summer and cold winter zones, which includes cold and thermal energy storage to improve building energy flexibility. The system capacity and load demand are analyzed, and the characteristics of energy-flexibility with and without energy storage are presented.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Computer Science, Theory & Methods
Shouyong Jiang, Juan Zou, Shengxiang Yang, Xin Yao
Summary: Evolutionary dynamic multi-objective optimisation (EDMO) is a rapidly growing area that uses evolutionary approaches to solve multi-objective optimisation problems with time-varying changes. After nearly two decades, significant advancements have been made in theoretic research and applications. This article provides a comprehensive survey and taxonomy of existing research on EDMO, as well as highlighting multiple research opportunities for further development.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Information Systems
Jialiang Zhang, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: This paper proposes a multi-modal multi-objective evolutionary algorithm (MMEAs) based on independently evolving sub-problems to solve the problem of poor diversity maintenance in traditional algorithms. A two-stage environmental selection strategy is used to ensure the convergence of the objective space and the distribution of the decision space. The k-nearest neighbor deletion strategy is employed in the decision space to guarantee the distributivity of each equivalent Pareto optimal solution.
INFORMATION SCIENCES
(2023)
Article
Mathematics
Qiuzhen Wang, Zhibing Liang, Juan Zou, Xiangdong Yin, Yuan Liu, Yaru Hu, Yizhang Xia
Summary: This study proposes a constrained multi-objective optimization evolutionary algorithm based on the dynamic constraint boundary method (CDCBM), which continuously searches for promising infeasible solutions to improve the diversity and convergence of the algorithm. Experimental results demonstrate that CDCBM performs competitively on various test suites and real-world problems.
Article
Mathematics
Ruiqiang Guo, Juan Zou, Qianqian Bai, Wei Wang, Xiaomeng Chang
Summary: This paper proposes a model named Community Detection Fusing Graph Attention Network (CDFG) to better utilize structural information and attribute information for community detection. By using an autoencoder to learn attribute features and a graph attention network to calculate the influence weight of neighborhood nodes and learn structural features, the CDFG model shows good performance in the experiments.
Article
Mathematics
Yizhang Xia, Jianzun Huang, Xijun Li, Yuan Liu, Jinhua Zheng, Juan Zou
Summary: This paper discusses the balance between convergence and diversity in many-objective evolutionary optimization algorithms. The authors propose a new algorithm called Indicator and Decomposition-based Evolutionary Algorithm (IDEA) to achieve both convergence and diversity. Experimental results show that IDEA outperforms other state-of-the-art many-objective algorithms.
Article
Computer Science, Information Systems
Yingjie Zou, Yuan Liu, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: Sparse large scale multiobjective optimization problems (sparse LSMOPs) have a high degree of sparsity in the decision variables of their Pareto optimal solutions. Existing evolutionary algorithms for sparse LSMOPs fail to achieve sufficient sparsity due to inaccurate location of nonzero decision variables and lack of interaction between the locating process and optimizing process. To address this, a dynamic sparse grouping evolutionary algorithm (DSGEA) is proposed, which groups decision variables with comparable numbers of nonzero variables and applies improved evolutionary operators for optimization. DSGEA outperforms current EAs in experiments on real-world and benchmark problems, achieving sparser Pareto optimal solutions with precise locations of nonzero decision variables.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Yaru Hu, Jinhua Zheng, Shouyong Jiang, Shengxiang Yang, Juan Zou
Summary: This article proposes an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multiobjective optimization (DMO) environments. The algorithm predicts population changes in response to environmental changes and maintains a balance between population diversity and convergence.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Qishuang Wu, Juan Zou, Shengxiang Yang, Yaru Hu
Summary: Responding quickly to environmental changes is crucial in solving dynamic multi-objective optimization problems (DMOPs). Most existing methods perform well on predicting individuals but struggle with improving the accuracy of the predicted population. This paper proposes an approach called RVCP, which combines an adjusted reference vector with a multi-objective evolutionary algorithm to predict the population and effectively tackle DMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Kaixi Yang, Jinhua Zheng, Juan Zou, Fan Yu, Shengxiang Yang
Summary: This paper proposes a dual-population algorithm called dp-ACS to balance constraint satisfaction and objective optimization. The algorithm introduces a dominance relation and an adaptive constraint strength strategy to improve convergence and consider excellent infeasible solutions. Experimental results show that the proposed algorithm outperforms seven state-of-the-art CMOEAs on constrained test suites and real-world CMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Zhenfang Du, Juan Zou, Shengxiang Yang
Summary: In researching multi-objective evolutionary algorithms, a preference-based MOEA called MOEA/D-ND is proposed. It uses a normal distribution to generate a weight vector and incorporates the decision-maker's preference information to guide convergence. An angle-based niche selection strategy is adopted to prevent falling into local optima. Experimental results show that this algorithm outperforms in various benchmark problems with 2 to 15 goals.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Fei Zhou, Juan Zou, Shengxiang Yang, Yaru Hu
Summary: This paper proposes a novel dynamic optimization strategy, called the prediction and diversity strategy (HPPDS), for solving multi-objective optimization problems. The HPPDS strategy combines prediction and diversity to cope with both predictable and unpredictable problems, enabling the optimization algorithm to adapt to various environmental changes. Experimental results show that HPPDS is competitive.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Juan Zou, Jian Luo, Yuan Liu, Shengxiang Yang, Jinhua Zheng
Summary: The core element in solving CMOPs is to balance objective optimization and constraint satisfaction. We propose a flexible two-stage evolutionary algorithm based on automatic regulation (ARCMO) to adapt to complex CMOPs.
INFORMATION SCIENCES
(2023)
