TY - JOUR
T1 - A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies
AU - Peraza-Vázquez, Hernán
AU - Peña-Delgado, Adrián F.
AU - Echavarría-Castillo, Gustavo
AU - Morales-Cepeda, Ana Beatriz
AU - Velasco-Álvarez, Jonás
AU - Ruiz-Perez, Fernando
N1 - Publisher Copyright:
© 2021 Hernán Peraza-Vázquez et al.
PY - 2021
Y1 - 2021
N2 - A novel bio-inspired algorithm, namely, Dingo Optimization Algorithm (DOA), is proposed for solving optimization problems. The DOA mimics the social behavior of the Australian dingo dog. The algorithm is inspired by the hunting strategies of dingoes which are attacking by persecution, grouping tactics, and scavenging behavior. In order to increment the overall efficiency and performance of this method, three search strategies associated with four rules were formulated in the DOA. These strategies and rules provide a fine balance between intensification (exploitation) and diversification (exploration) over the search space. The proposed method is verified using several benchmark problems commonly used in the optimization field, classical design engineering problems, and optimal tuning of a Proportional-Integral-Derivative (PID) controller are also presented. Furthermore, the DOA's performance is tested against five popular evolutionary algorithms. The results have shown that the DOA is highly competitive with other metaheuristics, beating them at the majority of the test functions.
AB - A novel bio-inspired algorithm, namely, Dingo Optimization Algorithm (DOA), is proposed for solving optimization problems. The DOA mimics the social behavior of the Australian dingo dog. The algorithm is inspired by the hunting strategies of dingoes which are attacking by persecution, grouping tactics, and scavenging behavior. In order to increment the overall efficiency and performance of this method, three search strategies associated with four rules were formulated in the DOA. These strategies and rules provide a fine balance between intensification (exploitation) and diversification (exploration) over the search space. The proposed method is verified using several benchmark problems commonly used in the optimization field, classical design engineering problems, and optimal tuning of a Proportional-Integral-Derivative (PID) controller are also presented. Furthermore, the DOA's performance is tested against five popular evolutionary algorithms. The results have shown that the DOA is highly competitive with other metaheuristics, beating them at the majority of the test functions.
UR - http://www.scopus.com/inward/record.url?scp=85116686044&partnerID=8YFLogxK
U2 - 10.1155/2021/9107547
DO - 10.1155/2021/9107547
M3 - Artículo
AN - SCOPUS:85116686044
SN - 1024-123X
VL - 2021
JO - Mathematical Problems in Engineering
JF - Mathematical Problems in Engineering
M1 - 9107547
ER -