Results 151 to 160 of about 8,461 (201)
Corrugation at the Trailing Edge Enhances the Aerodynamic Performance of a Three-Dimensional Wing During Gliding Flight. [PDF]
Biomimetics (Basel)Li K, Xu N, Zhong L, Mou X.
europepmc +1 more source
Padding interpolation, median imputation, RobustScalar, and particle swarm optimization with heterogeneous classifiers: a robust combination for effective heart disease diagnosis. [PDF]
Front Med (Lausanne)Dhanka S +7 more
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Dragonfly algorithm: a comprehensive review and applications
Neural Computing and Applications, 2020Dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic and static swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared to several well-known meta-heuristics available in the literature.
Meraihi, Yassine +3 more
openaire +3 more sources
An improved Dragonfly Algorithm for feature selection
Knowledge-Based Systems, 2020Abstract Dragonfly Algorithm (DA) is a recent swarm-based optimization method that imitates the hunting and migration mechanisms of idealized dragonflies. Recently, a binary DA (BDA) has been proposed. During the algorithm iterative process, the BDA updates its five main coefficients using random values.
Abdelaziz I. Hammouri +4 more
openaire +1 more source
Wind driven dragonfly algorithm for global optimization
Concurrency and Computation: Practice and Experience, 2020SummaryDragonfly algorithm (DA) is a new swarm intelligence optimization algorithm based on the static and dynamic swarm behavior of dragonflies. The algorithm has the characteristics of simple structure, strong search ability, easy implementation, and strong robustness.
Lianlian Zhong +3 more
openaire +1 more source
2017
The dragonfly algorithm (DA) is a new metaheuristic optimization algorithm, which is based on simulating the swarming behavior of dragonfly individuals. This algorithm was developed by Mirjalili (2016) and the preliminary studies illustrated its potential in solving numerous benchmark optimization problems and complex computational fluid dynamics (CFD)
Babak Zolghadr-Asli +2 more
openaire +1 more source
The dragonfly algorithm (DA) is a new metaheuristic optimization algorithm, which is based on simulating the swarming behavior of dragonfly individuals. This algorithm was developed by Mirjalili (2016) and the preliminary studies illustrated its potential in solving numerous benchmark optimization problems and complex computational fluid dynamics (CFD)
Babak Zolghadr-Asli +2 more
openaire +1 more source
Dragonfly algorithm for Solving Flexible Jobshop Scheduling Problem
2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2020Industrial scheduling problem has always been one of key problems in manufacturing and management planning enterprises. Flexible job shop scheduling problem is one of most difficult scheduling problems, which is also a classical NP hard problem. Compared to the JSP, each process in the FJSP should be processed on each available machine to minimize the ...
Dongsheng Yang +4 more
openaire +1 more source
Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection
Applied Intelligence, 2018Selecting the most discriminative features is a challenging problem in many applications. Bio-inspired optimization algorithms have been widely applied to solve many optimization problems including the feature selection problem. In this paper, the most discriminating features were selected by a new Chaotic Dragonfly Algorithm (CDA) where chaotic maps ...
Gehad Ismail Sayed +2 more
openaire +1 more source
Multi-objective Optimal PMU Placement using Binary Dragonfly Algorithm
2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2021Multi-objective optimal placement of PMU problem has been investigated. Conflicting objective functions such as minimization of count of PMUs and maximization of redundancy have been considered. Fuzzy membership based two different approaches such as maximum fuzzy satisfaction and maximum degree of satisfaction have been utilized to convert multi ...
C. D. Patel +2 more
openaire +1 more source