ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for Continuous Variables

TL;DR

This paper introduces ASOC, an adaptive, parameter-free stochastic optimization method designed for continuous variables, eliminating the need for user-defined parameters and adapting to changing search spaces.

Contribution

The paper presents ASOC, a novel adaptive stochastic optimization technique that is parameter-free and suitable for non-convex problems with continuous variables.

Findings

ASOC effectively adapts to changing search spaces.

ASOC outperforms traditional algorithms requiring parameter tuning.

ASOC reduces the need for iterative fine-tuning of parameters.

Abstract

Stochastic optimization is an important task in many optimization problems where the tasks are not expressible as convex optimization problems. In the case of non-convex optimization problems, various different stochastic algorithms like simulated annealing, evolutionary algorithms, and tabu search are available. Most of these algorithms require user-defined parameters specific to the problem in order to find out the optimal solution. Moreover, in many situations, iterative fine-tunings are required for the user-defined parameters, and therefore these algorithms cannot adapt if the search space and the optima changes over time. In this paper we propose an \underline{a}daptive parameter-free \underline{s}tochastic \underline{o}ptimization technique for \underline{c}ontinuous random variables called ASOC.