A Novel PSO-DE Co-evolutionary Algorithm Based on Decomposition Framework

Yang, Shaoqiang; Wang, Wenjun; Lin, Qiuzhen; Chen, Jianyong

doi:10.1007/978-3-319-52015-5_39

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…To make the evolutionary search converge faster, researchers have proposed many methods, including modification of evolutionary operators [31], using elite archive [32], ensembles [33] to increase the chance of selecting better parents, and niching methods [34] for local exploitation [35]. In terms of population diversity, some approaches have designed for escaping local optima and improving the performance of exploration [36]. Regarding the above two issues, in this research we will propose a novel GA with an elite archive for increasing convergence and a mating selection strategy which allows one parent to be selected from the whole population for increasing diversity.…”

Section: Evolutionary Computationmentioning

confidence: 99%

A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks

Yuan¹,

Wang²,

Coghill³

et al. 2021

Preprint

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Section: Evolutionary Computationmentioning

confidence: 99%

A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks

Yuan¹,

Wang²,

Coghill³

et al. 2021

Preprint

Self Cite

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“…In this way, particles will expand to farthest points in search space and have ability to converge faster than PSO . Moreover, researchers have already compared evolutionary algorithms like NSGA II (non‐dominated sorting genetic algorithm) with PSO‐DE for constrained optimization and found that PSO‐DE has performed better than NSGA II and other algorithms in literature. Moreover, PSO‐DE also has shown good results when compared with other PSO variants and DE variants …”

Section: Hierarchical Coordination and Optimization Frameworkmentioning

confidence: 99%

TSO and DSO with large‐scale distributed energy resources: A security constrained unit commitment coordinated solution

Nawaz

Wang

et al. 2019

Int Trans Electr Energ Syst

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Summary Modern power system requires optimally coordinated operation between transmission and distribution systems due to large‐scale integration of distributed energy resources (DERs). Commonly, transmission system operator (TSO) solves its own cost optimization problem and evaluates required targets for each distribution system operator (DSO). Thus, TSO problem becomes more complex and highly scaled with increase in number of connected distribution systems, which requires more computational time and resources. Therefore, this paper proposed a method to reduce computational burden on TSO and solve coordinated security constrained unit commitment (C‐SCUC) problem by involving a coordinator in upper level hierarchy. Coordinator is required to solve system‐wide problem to adjust targets for transmission system (TS) and all distribution systems (DS). Besides, TSO and DSO only need to solve their own cost optimization problems. In this paper, we have proposed analytical target cascading (ATC) along with hybridized particle swarm optimization with differential evolution (PSO‐DE) for solving increased complexity of the coordinated problem. ATC provides coordination between transmission and distribution sections of power system while hybrid PSO‐DE provides better accuracy of optimization in ATC layers. For evaluation of efficacy of proposed method, this paper has taken IEEE 118‐bus system as TS and IEEE 33‐bus system as DS. Results has depicted that proposed method could help TSO in reducing computational resources and improving computational time.

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Symmetric uncertainty based decomposition multi-objective immune algorithm for feature selection

Chai

2023

Swarm and Evolutionary Computation

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A Novel PSO-DE Co-evolutionary Algorithm Based on Decomposition Framework

Cited by 3 publications

References 17 publications

A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks

A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks

TSO and DSO with large‐scale distributed energy resources: A security constrained unit commitment coordinated solution

Symmetric uncertainty based decomposition multi-objective immune algorithm for feature selection

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