Results 11 to 20 of about 609,298 (293)
A self‐organizing weighted optimization based framework for large‐scale multi‐objective optimization
Swarm and Evolutionary Computation, 2022 The solving of large-scale multi-objective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, this paper proposes a self-organizing weighted optimization based framework, denoted S-WOF, for addressing LSMOPs.
Yongfeng Li +5 more
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Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model [PDF]
International Conference on Evolutionary Multi-Criterion Optimization, 2021 The main feature of large-scale multi-objective optimization problems (LSMOP) is to optimize multiple conflicting objectives while considering thousands of decision variables at the same time. An efficient LSMOP algorithm should have the ability to escape the local optimal solution from the huge search space and find the global optimal.
Hong, Haokai +4 more
semanticscholar +5 more sources
Soft Computing, 2023 AbstractEvaluating large-scale multi-objective problems is usually time-consuming due to a large number of decision variables. However, most of the existing algorithms for large-scale multi-objective optimization require a large number of problem evaluations to obtain acceptable results, which makes the optimization very inefficient.
Zhe Liu +4 more
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Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses
International Journal of Automation and Computing, 2021 Large-scale multi-objective optimization problems (MOPs) that involve a large number of decision variables, have emerged from many real-world applications. While evolutionary algorithms (EAs) have been widely acknowledged as a mainstream method for MOPs,
W. Hong, Peng Yang, K. Tang
semanticscholar +1 more source
Frontiers in Energy Research, 2023 To fully tap the abilities of renewables in reactive power optimization, this paper develops a detailed model for the power regulation capabilities of wind turbines and photovoltaic units and studies their impact on the power system’s operation.
Xiping Ma +4 more
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Mathematical Biosciences and Engineering, 2022 Supply chain network is important for the enterprise to improve the operation and management, but has become more complicated to optimize in reality. With the consideration of multiple objectives and constraints, this paper proposes a constrained large ...
Xin Zhang +4 more
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Complex & Intelligent Systems, 2023 Due to the exponential overflow of textual information in various fields of knowledge and on the internet, it is very challenging to extract important information or to generate a summary from some multi-document collection in a specific field. With such
H. Abo-Bakr, S. A. Mohamed
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Frontiers in Energy Research, 2022 Multi-objective optimization problems (MOPs) are commonly confronted in various fields, such as condition monitoring for renewable energy systems, and ratio error estimation of voltage transformers.
Jun Li, Kai Zou, Lining Xing
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Multi-objective Optimization Method Based on Reinforcement Learning in Multi-domain SFC Deployment [PDF]
Jisuanji kexue, 2021 With the development of network virtualization technology,the deployment of service function chain in multi-domain network brings new challenges to the optimization of service function chain.The traditional deployment method usually optimizes a single ...
WANG Ke, QU Hua, ZHAO Ji-hong
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Comparative evaluation of large-scale many objective algorithms on complex optimization problems [PDF]
Iranian Journal of Numerical Analysis and OptimizationIn the field of optimization, there has been an enormous surge in interest in addressing large-scale many-objective problems. Numerous academicians and practitioners have contributed to evolutionary computation by developing a variety of optimization ...
R. Chaudhary, A. Prajapati
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