@Article{cmes.2023.026231,
AUTHOR = {Wenchuan Wang, Weican Tian, Kwok-wing Chau, Yiming Xue, Lei Xu, Hongfei Zang},
TITLE = {An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {136},
YEAR = {2023},
NUMBER = {2},
PAGES = {1603--1642},
URL = {http://www.techscience.com/CMES/v136n2/51589},
ISSN = {1526-1506},
ABSTRACT = {The Bald Eagle Search algorithm (BES) is an emerging meta-heuristic algorithm. The algorithm simulates the
hunting behavior of eagles, and obtains an optimal solution through three stages, namely selection stage, search
stage and swooping stage. However, BES tends to drop-in local optimization and the maximum value of search
space needs to be improved. To fill this research gap, we propose an improved bald eagle algorithm (CABES) that
integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.
Firstly, CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage, to select a
better search range. Secondly, in the search stage, CABES updates the search position update formula by an adaptive
weight factor to further promote the local optimization capability of BES. To verify the performance of CABES, the
benchmark function of CEC2017 is used to simulate the algorithm. The findings of the tests are compared to those
of the Particle Swarm Optimization algorithm (PSO), Whale Optimization Algorithm (WOA) and Archimedes
Algorithm (AOA). The experimental results show that CABES can provide good exploration and development
capabilities, and it has strong competitiveness in testing algorithms. Finally, CABES is applied to four constrained
engineering problems and a groundwater engineering model, which further verifies the effectiveness and efficiency
of CABES in practical engineering problems.},
DOI = {10.32604/cmes.2023.026231}
}