Novel meta-heuristic bald eagle search optimisation algorithm

This study proposes a bald eagle search (BES) algorithm, which is a novel, nature-inspired meta-heuristic optimisation algorithm that mimics the hunting strategy or intelligent social behaviour of bald eagles as they search for fish. Hunting by BES is divided into three stages. In the first stage (s...

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Bibliographic Details
Published in:The Artificial intelligence review Vol. 53; no. 3; pp. 2237 - 2264
Main Authors: Alsattar, H. A., Zaidan, A. A., Zaidan, B. B.
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.03.2020
Springer
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Bald eagle
Birds
Computation
Computer Science
Fish
Heuristic
Heuristic methods
Hunting
Intelligence
Mathematical optimization
Novels
Optimization
Optimization algorithms
Performance evaluation
Rank tests
Searching
Social behavior
Meta-heuristic algorithm
Optimisation
Bald eagle behaviour
Unconstrained benchmark problem
ISSN:0269-2821, 1573-7462
Online Access:Get full text
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Summary:This study proposes a bald eagle search (BES) algorithm, which is a novel, nature-inspired meta-heuristic optimisation algorithm that mimics the hunting strategy or intelligent social behaviour of bald eagles as they search for fish. Hunting by BES is divided into three stages. In the first stage (selecting space), an eagle selects the space with the most number of prey. In the second stage (searching in space), the eagle moves inside the selected space to search for prey. In the third stage (swooping), the eagle swings from the best position identified in the second stage and determines the best point to hunt. Swooping starts from the best point and all other movements are directed towards this point. BES is tested by adopting a three-part evaluation methodology that (1) describes the benchmarking of the optimisation problem to evaluate the algorithm performance, (2) compares the algorithm performance with that of other intelligent computation techniques and parameter settings and (3) evaluates the algorithm based on mean, standard deviation, best point and Wilcoxon signed-rank test statistic of the function values. Optimisation results and discussion confirm that the BES algorithm competes well with advanced meta-heuristic algorithms and conventional methods.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-019-09732-5