A synergistic enhancement of the Ivy algorithm for GAN-based imbalanced classification.

The Ivy Algorithm (IVYA), a swarm intelligence algorithm inspired by plant growth, presents a novel framework for optimization. To unlock its full potential in complex, high-dimensional problems, it is crucial to address the fundamental challenge of balancing exploration and exploitation, which can impact overall search efficiency and solution quality. To this end, this paper proposes an Enhanced Ivy Algorithm (E-IVYA) that integrates three synergistic mechanisms. First, a dynamic perturbation framework combining symmetric and asymmetric exploration is introduced to maintain population diversity. Second, a dynamic escape mechanism based on elite differential mutation is employed to prevent search stagnation and effectively escape from local optima. Third, an adaptive movement strategy inspired by the Sine-Cosine Algorithm is integrated to achieve a more adaptive balance between global exploration and local exploitation. The performance of the proposed E-IVYA was rigorously evaluated through two distinct phases. Initially, its optimization capabilities were benchmarked against a wide range of classic and advanced algorithms on the challenging IEEE CEC 2014 and 2017 test suites. Subsequently, its practical utility was validated by applying it to the complex task of automating the hyperparameter optimization of Generative Adversarial Networks (GANs) for imbalanced data classification. The experimental results demonstrate E-IVYA's superior performance. On the standard benchmarks, E-IVYA consistently ranked as a top-performing algorithm. In the practical application, the E-IVYA-optimized GAN model achieved a minority class F1-Score of 0.87 on the highly imbalanced Credit-Card Fraud dataset, significantly outperforming models augmented with standard techniques like SMOTE (0.71). These findings confirm that E-IVYA is a robust and efficient tool for tackling complex optimization problems, particularly in the domain of automated machine learning.