Optimum Design of Flapping Wing Flying Robot by Modified Social Group Optimization

Constrained optimization is very often appealed to handle challenging design problems in engineering area. Herein, heuristic methods are frequently preferred to solve these design problems. For the best design of an engineering problem, the robustness of optimization algorithm occupies an important...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) s. 1 - 5
Hlavní autori: Ocal, Aysun, Koyuncu, Hasan
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 03.10.2022
Predmet:
constrained optimization
Crystals
Design methodology
engineering design
Heuristic algorithms
modified social group optimization
ornithopter
Particle swarm optimization
penalty function
Reliability engineering
Robustness
Social groups
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Constrained optimization is very often appealed to handle challenging design problems in engineering area. Herein, heuristic methods are frequently preferred to solve these design problems. For the best design of an engineering problem, the robustness of optimization algorithm occupies an important place.In this paper, a recent engineering problem is handled which evaluates the optimum design of a flapping wing flying robot / ornithopter. Concerning the issue, main function and constrained functions are combined using penalty function to define the problem encountered as a single objective optimization problem. The design problem is evaluated by four recent and promising algorithms that are chaotic dynamic weight particle swarm optimization (CDW-PSO), crystal structure algorithm (CryStAl), adaptive strategy particle swarm optimization (ASPSO), and modified social group optimization (MSGO). The best fitness, processing time and average best fitness evaluations are considered to objectively reveal the most appropriate method for optimum design. Consequently, MSGO and ASPSO achieve the optimum results and outperforms CDW-PSO and CryStAl algorithms for the best fitness-based experiments. Moreover, MSGO yields a remarkable performance than ASPSO by presenting a more robust behavior for average best fitness-based comparisons.
DOI:10.1109/ICCCNT54827.2022.9984633