Multi-strategy enhanced Aquila optimization algorithm

Abstract

Although the Aquila optimization (AO) algorithm demonstrates promising results in various optimization tasks, it is still easy to fall into local optima, particularly when addressing more intricate problems. To tackle this issue, a reinforced tent-enhanced AO (RTEAO) algorithm that incorporates multiple strategic integrations was proposed in this paper. During the initialization phase, a tent chaotic mapping method is employed to enhance population diversity. To update individual positions iteratively, an elite reverse learning strategy and an optimal retention mechanism are employed to circumvent local optima. Experimental evaluations on the CEC2005 test functions demonstrate significant improvements in both convergence speed and accuracy. Furthermore, the RTEAO algorithm showcases remarkable optimization performance in practical applications, specifically in the calibration of a back propagation (BP) neural network for regression analysis on the Boston Housing Price Dataset and Wisconsin Breast Cancer Dataset, thereby affirming its efficacy and versatility in confronting real-world optimization challenges.

Keywords

Aquila optimization (AO) / reinforced tent-enhanced AO (RTEAO) / tent chaotic mapping / elite reverse learning / optimal retention mechanism / back propagation (BP) neural network

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