Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications

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...

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Vydáno v:Computer methods in applied mechanics and engineering Ročník 388; s. 114194
Hlavní autoři: Zhao, Weiguo, Wang, Liying, Mirjalili, Seyedali
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.01.2022
Elsevier BV
Témata:
Algorithm
Algorithms
Artificial hummingbird algorithm
Benchmark
Bio-inspired computing
Design engineering
Engineering optimization
Foraging behavior
Genetic algorithm
Heuristic methods
Meta-heuristics
Optimization
Optimization techniques
Skills
Source code
Swarm intelligence
Artificial hummingbird algorithm
Genetic algorithm
Engineering optimization
Benchmark
Swarm intelligence
Bio-inspired computing
Algorithm
Meta-heuristics
ISSN:0045-7825, 1879-2138
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Shrnutí: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.
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ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2021.114194