Open Access

ARTICLE

An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

by Wenchuan Wang^1,*, Weican Tian¹, Kwok-wing Chau², Yiming Xue¹, Lei Xu³, Hongfei Zang¹

1 College of Water Resources, Henan Key Laboratory of Water Resources Conservation and Intensive Utilization in the Yellow River Basin, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
2 Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
3 College of Hydrology and Water Resources, Hohai University, Nanjing, 210024, China

* Corresponding Authors: Wenchuan Wang. Email: ;

(This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)

Computer Modeling in Engineering & Sciences 2023, 136(2), 1603-1642. https://doi.org/10.32604/cmes.2023.026231

Received 25 August 2022; Accepted 08 December 2022; Issue published 06 February 2023

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.

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