TY - JOUR
T1 - Artificial hummingbird algorithm
T2 - A new bio-inspired optimizer with its engineering applications
AU - Zhao, Weiguo
AU - Wang, Liying
AU - Mirjalili, Seyedali
N1 - Funding Information:
The authors sincerely thank the anonymous reviewers for their valuable and insightful comments and suggestions. The authors would like to thank Dr. Jason of University of Illinois at Urbana-Champaign for editorial review. This work was supported in part by the National Natural Science Foundation of China ( 11972144 and 12072098 ), and the One Hundred Outstanding Innovative Scholars of Colleges and Universities in Hebei Province of China ( SLRC2019022 ).
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - A new bio-inspired optimization algorithm called artificial hummingbird algorithm (AHA) is proposed in this work to solve optimization problems. The AHA algorithm simulates the special flight skills and intelligent foraging strategies of hummingbirds in nature. Three kinds of flight skills utilized in foraging strategies, including axial, diagonal, and omnidirectional flights, are modeled. In addition, guided foraging, territorial foraging, and migrating foraging are implemented, and a visit table is constructed to model the memory function of hummingbirds for food sources. AHA is validated using two sets of numerical test functions, and the results are compared with those obtained from various algorithms. The comparisons demonstrate that AHA is more competitive than other meta-heuristic algorithms and determine high-quality solutions with fewer control parameters. Additionally, the performance of AHA is validated on ten challenging engineering design cases studies. The results show the superior effectiveness of AHA in terms of computational burden and solution precision compared with the existing optimization techniques in literature. The study also explores the application of AHA in hydropower operation design to further demonstrate its potential in practice. The source code of AHA is publicly available at https://seyedalimirjalili.com/aha and https://www.mathworks.com/matlabcentral/fileexchange/101133-artificial-hummingbird-algorithm?s_tid=srchtitle.
AB - A new bio-inspired optimization algorithm called artificial hummingbird algorithm (AHA) is proposed in this work to solve optimization problems. The AHA algorithm simulates the special flight skills and intelligent foraging strategies of hummingbirds in nature. Three kinds of flight skills utilized in foraging strategies, including axial, diagonal, and omnidirectional flights, are modeled. In addition, guided foraging, territorial foraging, and migrating foraging are implemented, and a visit table is constructed to model the memory function of hummingbirds for food sources. AHA is validated using two sets of numerical test functions, and the results are compared with those obtained from various algorithms. The comparisons demonstrate that AHA is more competitive than other meta-heuristic algorithms and determine high-quality solutions with fewer control parameters. Additionally, the performance of AHA is validated on ten challenging engineering design cases studies. The results show the superior effectiveness of AHA in terms of computational burden and solution precision compared with the existing optimization techniques in literature. The study also explores the application of AHA in hydropower operation design to further demonstrate its potential in practice. The source code of AHA is publicly available at https://seyedalimirjalili.com/aha and https://www.mathworks.com/matlabcentral/fileexchange/101133-artificial-hummingbird-algorithm?s_tid=srchtitle.
KW - Algorithm
KW - Artificial hummingbird algorithm
KW - Benchmark
KW - Bio-inspired computing
KW - Engineering optimization
KW - Genetic algorithm
KW - Meta-heuristics
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85118900285&partnerID=8YFLogxK
U2 - 10.1016/j.cma.2021.114194
DO - 10.1016/j.cma.2021.114194
M3 - Article
AN - SCOPUS:85118900285
SN - 0045-7825
VL - 388
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
M1 - 114194
ER -