• Rajani Kumar Sindavalam
• Nagaraju Dodda

Deep learning optimization has witnessed significant advancements with the introduction of adaptive and nature-inspired algorithms. However, existing methods, such as SGD, Adam, and RMSprop, struggle with either slow convergence or instability in complex loss landscapes. This research presents Adaptive Lion Lookahead Optimization (ALLO)—a novel hybrid ensemble technique integrating the Adaptive Lion Swarm Algorithm (ALSA) and Lookahead Optimizer (LAO). ALSA improves exploration-exploitation balance by dynamically adjusting Peritoneal Dialysis (PD) Cyclers strategies, while LAO enhances gradient-based updates for smoother convergence. The proposed ALLO technique was evaluated on three benchmark datasets: CIFAR-10, ImageNet (Subset), and UCI Parkinson's dataset. Results show that ALLO achieves 96.7% accuracy on CIFAR-10, 83.8% on ImageNet, and 92.5% on UCI Parkinson's dataset, outperforming traditional optimizers by 4.4%to 7.4%. Furthermore, ALLO reduces training time by 48% compared to SGD and improves convergence speed by 44% over Adam. With lower gradient variance ( 0.31vs.0.78 in SGD), ALLO offers more stable updates, reducing oscillations and improving generalization. The hybrid nature of ALLO makes it suitable for high-dimensional, nonconvex deep learning tasks, offering superior speed, stability, and accuracy.

: Deep Learning Optimization, Adaptive Lion Swarm Algorithm (ALSA), Lookahead Optimizer (LAO), Hybrid Optimization Techniques, Gradient-Free Learning, Exploration-Exploitation Balance, Metaheuristic Algorithms for AI

"Adaptive Lion Lookahead Optimization (ALLO): A Hybrid Deep Learning Optimizer Integrating Gradient-Free Exploration and Structured Convergence for Peritoneal Dialysis (PD) Cyclers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.f473-f484, March-2025, Available :http://www.jetir.org/papers/JETIR2503584.pdf

"Adaptive Lion Lookahead Optimization (ALLO): A Hybrid Deep Learning Optimizer Integrating Gradient-Free Exploration and Structured Convergence for Peritoneal Dialysis (PD) Cyclers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppf473-f484, March-2025, Available at : http://www.jetir.org/papers/JETIR2503584.pdf