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...

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Vydáno v:2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) s. 1 - 5
Hlavní autoři: Ocal, Aysun, Koyuncu, Hasan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 03.10.2022
Témata:
constrained optimization
Crystals
Design methodology
engineering design
Heuristic algorithms
modified social group optimization
ornithopter
Particle swarm optimization
penalty function
Reliability engineering
Robustness
Social groups
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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