TY - JOUR
T1 - A new movement strategy of grey wolf optimizer for optimization problems and structural damage identification
AU - Sang-To, Thanh
AU - Le-Minh, Hoang
AU - Mirjalili, Seyedali
AU - Abdel Wahab, Magd
AU - Cuong-Le, Thanh
N1 - Funding Information:
The authors acknowledge the financial support of the VLIR-UOS TEAM Project, VN2018TEA479A103 , ‘Damage assessment tools for Structural Health Monitoring of Vietnamese infrastructures’, funded by the Flemish Government . The authors wish to express their gratitude to Van Lang University, Vietnam for financial support for this research.
Publisher Copyright:
© 2022
PY - 2022/11
Y1 - 2022/11
N2 - In this paper, an improved Grey Wolf Optimizer (GWO) algorithm, termed LGWO, is introduced. The enhanced version is interesting and complementary in terms of the direction of movement of the leader wolf, and a special parameter that allows the faster wolves to prey position. The Lévy flight is employed as a special navigation solution for alpha, beta, and delta wolf. In this way, the leader wolf equips a powerful tool to deal with the local search problem. A new principle illustrates the behaviour of omega wolf in hunting is also added to enhance the convergence speed of this algorithm. To investigate the performance of LGWO, a series of problems, namely 23 classical benchmarks, a set of CEC 2019 functions, and three engineering problems, is investigated. Furthermore, LGWO is employed to study structural damage identification in high-dimensional problems. The research appears to show that the performance of LGWO is substantially increased.
AB - In this paper, an improved Grey Wolf Optimizer (GWO) algorithm, termed LGWO, is introduced. The enhanced version is interesting and complementary in terms of the direction of movement of the leader wolf, and a special parameter that allows the faster wolves to prey position. The Lévy flight is employed as a special navigation solution for alpha, beta, and delta wolf. In this way, the leader wolf equips a powerful tool to deal with the local search problem. A new principle illustrates the behaviour of omega wolf in hunting is also added to enhance the convergence speed of this algorithm. To investigate the performance of LGWO, a series of problems, namely 23 classical benchmarks, a set of CEC 2019 functions, and three engineering problems, is investigated. Furthermore, LGWO is employed to study structural damage identification in high-dimensional problems. The research appears to show that the performance of LGWO is substantially increased.
KW - CEC 2019
KW - GWO
KW - LGWO
KW - Optimization
KW - Structural health monitoring
UR - http://www.scopus.com/inward/record.url?scp=85137166188&partnerID=8YFLogxK
U2 - 10.1016/j.advengsoft.2022.103276
DO - 10.1016/j.advengsoft.2022.103276
M3 - Article
AN - SCOPUS:85137166188
VL - 173
JO - Advances in Engineering Software
JF - Advances in Engineering Software
SN - 0965-9978
M1 - 103276
ER -