Nan-Jiang Dong 0001

dblp:262/2489 · also Nanjiang Dong 0001 · DBLP profile ↗
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4ranked-venue papers
2as first author
4since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 1 first-author · 3 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021
YearPublicationVenuePosition
2024 An evolutionary algorithm based on fully connected weight networks for mixed-variable multi-objective optimization
Nan-Jiang Dong 0001, Tao Zhang 0033, Rui Wang 0017, Xiangke Liao, Ling Wang 0001
Inf. Sci.1
2023 Cooperative coevolutionary competition swarm optimizer with perturbation for high-dimensional multi-objective optimization
Rui Wang 0017, Tao Zhang 0033, Nan-Jiang Dong 0001
Inf. Sci.4
2022 Individual-based self-learning prediction method for dynamic multi-objective optimization
Junwei Ou, Lining Xing 0001, Jimin Lv, Yaru Hu, Nan-Jiang Dong 0001, Guoting Zhang
Inf. Sci.6
2021 A New Encoding Mechanism Embedded Evolutionary Algorithm for UAV Route Planning
abstract
Evolutionary algorithms (EAs) are often applied to deal with UAV route planning. The solution encoding is one of important factor in designing effective EAs. In a traditional encoding mechanism, each individual represents one route. The whole population then consists of a number of routes. We argue that such an encoding is less effective in route planning, and then proposed an alternative encoding mechanism in which one individual represents only one navigation point. The whole population then represents one route. This implicitly turns EAs into single-point based search with high exploitation ability. To further improve the exploration ability of algorithms using this new encoding, a slightly modified differential evolution operator is applied. Combining the modified DE operator and the new encoding mechanism, the performance of the derived algorithm is significantly improved, obtaining much better route planning results than DE with the traditional encoding mechanism.
Nan-Jiang Dong 0001, Rui Wang 0017, Tao Zhang 0033
CEC1