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

The Bald Eagle Search algorithm (BES) is an emerging meta-heuristic algorithm. simulates the hunting behavior of eagles, and obtains optimal solution through three stages, namely selection stage, search stage swooping stage. However, BES tends to drop-in local optimization maximum value space needs be improved. To fill this research gap, we propose improved bald eagle (CABES) that integrates Cauchy mutation adaptive improve performance from optima. Firstly, CABES introduces strategy adjust step size select a better range. Secondly, in updates position update formula by weight factor further promote capability BES. verify CABES, benchmark function CEC2017 used simulate findings tests are compared those Particle Swarm Optimization (PSO), Whale Algorithm (WOA) Archimedes (AOA). experimental results show can provide good exploration development capabilities, it has strong competitiveness testing algorithms. Finally, applied four constrained engineering problems groundwater model, which verifies effectiveness efficiency practical problems.