Article
Computer Science, Artificial Intelligence
Xiaobing Yu, Wenguan Luo, WangYing Xu, ChenLiang Li
Summary: This study addresses the issue of selecting feasible and infeasible solutions in Constrained Multi-objective Optimization Problems (CMOPs) by developing a constrained multi-objective Differential Evolution (DE) algorithm. The experiments demonstrate that the algorithm can find well-distributed Pareto front and achieve superior performance indicator results.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jing Liang, Xuanxuan Ban, Kunjie Yu, Kangjia Qiao, Boyang Qu
Summary: This paper presents a constrained multiobjective differential evolution algorithm with an infeasible-proportion control mechanism, which addresses the handling of conflicting objectives and constraints through cooperative strategies and infeasible-proportion control. Experimental results demonstrate that the proposed algorithm outperforms or is at least comparable to existing constrained multiobjective optimization methods on various benchmark test functions.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Xue Feng, Anqi Pan, Zhengyun Ren, Zhiping Fan
Summary: Balancing convergence and diversity is a challenge in multi-objective optimization problems, especially when the proportion of feasible regions is low. This paper proposes a constrained multi-objective optimization algorithm based on a hybrid driven strategy to enhance the feasibility and diversity performance of Pareto solutions. The algorithm outperforms peer algorithms, especially in large-infeasible-regions multi-objective optimization problems.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Vikas Palakonda, Jae-Mo Kang
Summary: This article proposes a preference-inspired differential evolution algorithm for multi and many-objective optimization, which effectively deals with a wide range of problems. The algorithm generates individuals with good convergence and distribution properties by utilizing a preference-inspired mutation operator and determining local knee points based on a clustering method. Experimental results demonstrate its superior performance compared to eight state-of-the-art algorithms on 35 benchmark problems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Jianchang Liu, Shubin Tan
Summary: The paper introduces a multi objective differential evolutionary algorithm based on partition selection (MODE-PS) to tackle constrained multi-objective optimization problems. By dividing problems into sub-spaces and maintaining feasibility, the algorithm accelerates convergence and proves to be competitive in solving CMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Maha Elarbi, Slim Bechikh, Lamjed Ben Said
Summary: The paper proposes the ISC-Pareto dominance relation for handling constrained many-objective problems, and integrates it into the framework of the Constrained Non-Dominated Sorting Genetic Algorithm-III to create a new algorithm called ISC-NSGA-III. Empirical results demonstrate the effectiveness of the constraint handling strategy and the algorithm in solving multi-objective evolutionary algorithms.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Jianchang Liu, Shubin Tan
Summary: Many MOEAs are developed to solve CMOPs, but they encounter low efficiency for steady-state CMOPs. This paper proposes a multi-objective evolutionary algorithm named FACE, which maintains the known feasible solution in the second population and evolves together with the main population. Performance comparisons show the efficiency and scalability of FACE for steady-state CMOPs.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Jian Feng, Shaoning Liu, Shengxiang Yang, Jun Zheng, Jinze Liu
Summary: Convergence, diversity, and feasibility are crucial factors in solving constrained multi-objective optimization problems. This paper proposes an adaptive tradeoff evolutionary algorithm (ATEA) to achieve a balance between convergence and diversity while ensuring population feasibility.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Zhenzhen Hu, Wenyin Gong
Summary: This article proposes a differential evolution assisted by reinforcement learning (RL-CORCO) method for solving constrained optimization problems. By combining evolutionary algorithms with learning techniques, promising performance can be achieved. Experimental results show that RL-CORCO outperforms other methods on multiple benchmark problems.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Weixiong Huang, Juan Zou, Yuan Liu, Shengxiang Yang, Jinhua Zheng
Summary: This paper proposes a constrained multi-objective evolutionary algorithm framework based on global and local feasible solutions search to address the complexity of feasible regions caused by constraints. The framework is divided into three stages and an adaptive method is used to decide when to switch the search state. The experimental results show that the proposed framework is highly competitive for solving CMOPs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Yupeng Han, Hu Peng, Changrong Mei, Lianglin Cao, Changshou Deng, Hui Wang, Zhijian Wu
Summary: This paper proposes a new multistrategy multiobjective differential evolutionary algorithm, RLMMDE, to solve the exploration and exploitation dilemma in multiobjective optimization problems (MOPs). The algorithm utilizes a multistrategy and multicrossover DE optimizer, an adaptive reference point activation mechanism based on RL, and a reference point adaptation method. Experimental results show that RLMMDE outperforms some advanced MOEAs on benchmark test suites and practical mixed-variable optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiangsong Kong, Yongkuan Yang, Zhisheng Lv, Jing Zhao, Rong Fu
Summary: This paper proposes a dynamic dual-population co-evolution multi-objective evolutionary algorithm (DDCMEA) to address the issue of balancing feasibility, convergence, and diversity in constrained multi-objective optimization problems. DDCMEA employs a dynamic dual-population co-evolution strategy to balance convergence and feasibility by adjusting the offspring number of the two populations. In the early stage, the algorithm focuses on convergence and generates more offspring of the first population, while in the late stage, it focuses on feasibility and generates more offspring of the second population. The results show that DDCMEA achieves competitive performance in handling constrained multi-objective optimization problems.
APPLIED SOFT COMPUTING
(2023)
Article
Engineering, Chemical
Samira Ghorbanpour, Yuwei Jin, Sekyung Han
Summary: An adaptive Grid-based Multi-Objective Differential Evolution algorithm is proposed in this paper to address multi-objective optimization, aiming to improve algorithm performance by performing mutation strategy in a grid environment and considering performance metrics.
Article
Multidisciplinary Sciences
Mingwei Fan, Jianhong Chen, Zuanjia Xie, Haibin Ouyang, Steven Li, Liqun Gao
Summary: In this paper, an improved multi-objective differential evolution algorithm (MOEA/D/DEM) based on a decomposition strategy is proposed to enhance the search performance for practical multi-objective nutrition decision problems. The algorithm utilizes a neighborhood intimacy factor and a new Gaussian mutation strategy to improve diversity and local search ability. Experimental results show that the proposed algorithm achieves better search capability and obtains competitive results compared to other multi-objective algorithms.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Yong Wang, Zhen Liu, Gai-Ge Wang
Summary: Recently, multimodal multi-objective problem (MMOP) has attracted significant attention in the field of multi-objective optimization problems. The proposed algorithm addresses the issue of finding all equivalent Pareto sets in MMOP by introducing a modified maximum extension distance (MMED) indicator and implementing two-stage and novel mutation strategies. Additionally, a MMED-based environmental selection strategy improves the overall performance of the population.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Energy & Fuels
Haifeng Zhang, Feng Gao, Jiang Wu, Kun Liu, Xiaolin Liu
Article
Energy & Fuels
Wushan Cheng, Haifeng Zhang
Article
Green & Sustainable Science & Technology
Haifeng Zhang, Ting Xu, Hongyu Wu, Bo Liu, M. Nazif Faqiry
IET RENEWABLE POWER GENERATION
(2019)
Article
Computer Science, Artificial Intelligence
Bin Xu, Haifeng Zhang, Meihua Zhang, Lilan Liu
SWARM AND EVOLUTIONARY COMPUTATION
(2019)
Article
Chemistry, Multidisciplinary
Dawei Geng, Haifeng Zhang, Hongyu Wu
APPLIED SCIENCES-BASEL
(2020)
Article
Construction & Building Technology
Xuebo Liu, Yingying Wu, Haifeng Zhang, Hongyu Wu
Summary: This paper proposes a novel HVAC energy management scheme to optimize the thermostat setpoints of HVAC and provide recommendations on occupants' clothing decisions, considering uncertainties in outside temperature. Simulation results demonstrate significant cost savings with this scheme.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Thermodynamics
Haifeng Zhang, Ming Tian, Cong Zhang, Bin Wang, Dai Wang
Summary: This study proposes an integrated framework to quantify and utilize the aggregate flexibility of EVs to supply grid services in electricity markets. Simulation results show that the proposed optimization methods can significantly reduce system costs in both wholesale and retail markets. Optimization in wholesale markets indicates potential revenues of $691 and $255 per year for each EV in ERCOT and CAISO markets, respectively.
Article
Energy & Fuels
Ming Tian, Haifeng Zhang, Hongyu Wu
Summary: This study proposes a two-stage model for energy management in data center microgrids, aiming to reduce operating costs by optimizing workloads and waste heat while meeting temperature requirements for server clusters, and considering the potential for waste heat recovery and reuse.
IET ENERGY SYSTEMS INTEGRATION
(2022)