Results 251 to 260 of about 609,298 (293)
Some of the next articles are maybe not open access.
A multi-operator-based large-scale multi-objective optimization evolutionary algorithm
International Conference on Bioinformatics and Intelligent Computing, 2023Large-scale multi-objective optimization brings significant challenges for offspring generation, due to its multiple conflicting objectives and huge searching space.
Jie Cao +3 more
semanticscholar +1 more source
COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 2019
Purpose In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems.
Souhil Mouassa, Tarek Bouktir
openaire +1 more source
Purpose In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems.
Souhil Mouassa, Tarek Bouktir
openaire +1 more source
A cooperative co-evolutionary algorithm for large-scale multi-objective optimization problems
Annual Conference on Genetic and Evolutionary Computation, 2018A wide range of real-world problems are multi-objective optimization problems (MOPs). Multi-objective evolutionary algorithms (MOEAs) have been proposed to solve MOPs, but the search process deteriorates with the increase of MOPs' dimension of decision ...
Minghan Li, Jingxuan Wei
semanticscholar +1 more source
Large-scale Multi-objective Optimization for Water Quality in Chesapeake Bay Watershed
IEEE Congress on Evolutionary Computation, 2022The careful selection of Best Management Practices (BMPs) to reduce loading, such as nitrogen, phosphorus, and sediments, can substantially improve the water quality of water-sheds.
Gregorio Toscano +5 more
semanticscholar +1 more source
IEEE/CAA Journal of Automatica Sinica
Sparse large-scale multi-objective optimization problems (SLMOPs) are common in science and engineering. However, the large-scale problem represents the high dimensionality of the decision space, requiring algorithms to traverse vast expanse with limited
Sheng Qi +5 more
semanticscholar +1 more source
Sparse large-scale multi-objective optimization problems (SLMOPs) are common in science and engineering. However, the large-scale problem represents the high dimensionality of the decision space, requiring algorithms to traverse vast expanse with limited
Sheng Qi +5 more
semanticscholar +1 more source
IEEE/CAA Journal of Automatica Sinica
Traditional large-scale multi-objective optimization algorithms (LSMOEAs) encounter difficulties when dealing with sparse large-scale multi-objective optimization problems (SLM-OPs) where most decision variables are zero. As a result, many algorithms use
Sheng Qi +5 more
semanticscholar +1 more source
Traditional large-scale multi-objective optimization algorithms (LSMOEAs) encounter difficulties when dealing with sparse large-scale multi-objective optimization problems (SLM-OPs) where most decision variables are zero. As a result, many algorithms use
Sheng Qi +5 more
semanticscholar +1 more source
An improved problem transformation algorithm for large-scale multi-objective optimization
Swarm and Evolutionary ComputationYu Sun, Daijin Jiang
openaire +2 more sources
2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)
Sparse large-scale multi-objective optimization problems (SLSMOPs) hold significant practical relevance across various domains. However, the efficacy of existing evolutionary algorithms (EAs) in tackling these optimization challenges is limited due to ...
Xiaodong Huang +5 more
semanticscholar +1 more source
Sparse large-scale multi-objective optimization problems (SLSMOPs) hold significant practical relevance across various domains. However, the efficacy of existing evolutionary algorithms (EAs) in tackling these optimization challenges is limited due to ...
Xiaodong Huang +5 more
semanticscholar +1 more source
Large‐Scale Multi‐Objective Optimization Algorithms: A Decade Survey
Expert SystemsABSTRACT Large‐scale multi‐objective optimization problems (LSMOPs) are characterised by concurrent optimization of multiple conflicting objectives and no fewer than 100 decision variables. They widely exist in the fields of practical engineering and scientific research.
Pengtao Wang, Xiangjuan Wu, Hanqing Deng
openaire +1 more source
A Symmetric Points Search and Variable Grouping Method for Large-scale Multi-objective Optimization
IEEE Congress on Evolutionary Computation, 2020In this paper, we propose a new method for large scale multi-objective optimization based on symmetric points search and variable grouping, named SSVG.
Dandan Tang +3 more
semanticscholar +1 more source