SPGD: Steepest Perturbed Gradient Descent Optimization
This addresses optimization challenges for researchers and practitioners in scientific and industrial fields, offering a novel method to enhance convergence in complex landscapes, though it appears incremental as it builds on gradient descent with added perturbations.
The paper tackled the problem of optimization algorithms getting stuck in local minima, saddle points, and plateaus by introducing the Steepest Perturbed Gradient Descent (SPGD) algorithm, which combines gradient descent with periodic uniform perturbation sampling to escape sub-optimal solutions, resulting in substantial improvements over four established methods in solving the 3D component packing problem and outperforming benchmarks on 2D functions.
Optimization algorithms are pivotal in advancing various scientific and industrial fields but often encounter obstacles such as trapping in local minima, saddle points, and plateaus (flat regions), which makes the convergence to reasonable or near-optimal solutions particularly challenging. This paper presents the Steepest Perturbed Gradient Descent (SPGD), a novel algorithm that innovatively combines the principles of the gradient descent method with periodic uniform perturbation sampling to effectively circumvent these impediments and lead to better solutions whenever possible. SPGD is distinctively designed to generate a set of candidate solutions and select the one exhibiting the steepest loss difference relative to the current solution. It enhances the traditional gradient descent approach by integrating a strategic exploration mechanism that significantly increases the likelihood of escaping sub-optimal local minima and navigating complex optimization landscapes effectively. Our approach not only retains the directed efficiency of gradient descent but also leverages the exploratory benefits of stochastic perturbations, thus enabling a more comprehensive search for global optima across diverse problem spaces. We demonstrate the efficacy of SPGD in solving the 3D component packing problem, an NP-hard challenge. Preliminary results show a substantial improvement over four established methods, particularly on response surfaces with complex topographies and in multidimensional non-convex continuous optimization problems. Comparative analyses with established 2D benchmark functions highlight SPGD's superior performance, showcasing its ability to navigate complex optimization landscapes. These results emphasize SPGD's potential as a versatile tool for a wide range of optimization problems.