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Particle Swarm Assisted Genetic Algorithm for the Optimal Design of Flexbeam Sections
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 Title & Authors
Particle Swarm Assisted Genetic Algorithm for the Optimal Design of Flexbeam Sections
Dhadwal, Manoj Kumar; Lim, Kyu Baek; Jung, Sung Nam; Kim, Tae Joo;
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 Abstract
This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are revisited, and a new rank-based multi-parent crossover operator is utilized. The rank-based crossover operator simultaneously enhances both the local, and the global exploration. The benchmark results demonstrate remarkable improvements, in terms of efficiency and robustness, as compared to other state-of-the-art algorithms. The developed algorithm is adopted for two baseline flexbeam section designs, and optimum cross-section configurations are obtained with less function evaluations, and less computation time.
 Keywords
Beam Section Optimization;Real-Coded Genetic Algorithm;Particle Swarm Optimization;Rank-based Multi-Parent Crossover;Flexbeam;
 Language
English
 Cited by
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