Title: Adaptive bald eagle search algorithm with elite swarm guiding and population memory crossover

Authors: Dongmei Zhang; Xiaolei Liang; Yulian Cao; Mengdi Zhang; Di Xiao

Addresses: School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, China ' School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, China ' School of Modern Post, Beijing University of Posts and Telecommunications, China ' School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, China ' School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, China

Abstract: Bald eagle search (BES) algorithm is easy to fall into local optimums and the population diversity declines rapidly. To address the problems, an adaptive bald eagle search (ABES) algorithm with elite swarm guiding and population memory crossover is proposed. At the selecting stage, an elite population information is provided to replace a single information to guide location updating. At the searching stage, a population memory crossover strategy is proposed. The swooping stage is deleted in ABES due to search redundancy. Furthermore, the key parameters of ABES are designed to adjust adaptively to improve the population search ability. Finally, the test suite of CEC-2013 and three real-world engineering design problems are taken to test the performance of ABES, compared with seven state-of-art algorithms. The experimental results show that ABES has a much better comprehensive performance on convergence and local search capability than other selected algorithms.

Keywords: bald eagle search algorithm; elite swarm guidance; population memory; crossover recombination; adaptive.

DOI: 10.1504/IJBIC.2024.141680

International Journal of Bio-Inspired Computation, 2024 Vol.24 No.3, pp.133 - 149

Received: 12 Oct 2022
Accepted: 27 Nov 2023
Published online: 30 Sep 2024 *

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