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

Source: [ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for Continuous Variables](<https://www.sota2.com/research/paper/68fa61f91e209131ce9f6bd7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-14T16:36:10.241Z

Authors: Jayanta Basak

Published: 2015-06-26T09:13:57.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Mathematical Optimization on Beale function](<https://www.sota2.com/research/sota/mathematical-optimization-on-beale-function.md>): Mathematical Optimization; Beale function; Functional Minima: 0.

- [Mathematical Optimization on Booth function](<https://www.sota2.com/research/sota/mathematical-optimization-on-booth-function.md>): Mathematical Optimization; Booth function; Functional Minima: 0.0004.

- [Mathematical Optimization on Matyas function](<https://www.sota2.com/research/sota/mathematical-optimization-on-matyas-function.md>): Mathematical Optimization; Matyas function; Functional Minima: 0.00005.

- [Mathematical Optimization on Eggholder function](<https://www.sota2.com/research/sota/mathematical-optimization-on-eggholder-function.md>): Mathematical Optimization; Eggholder function; Functional Minima: -959.641.

- [Mathematical Optimization on Schaffer N. 2 function](<https://www.sota2.com/research/sota/mathematical-optimization-on-schaffer-n-2-function.md>): Mathematical Optimization; Schaffer N. 2 function; Functional Minima: 0.0005.

- [Mathematical Optimization on Schaffer N. 4 function](<https://www.sota2.com/research/sota/mathematical-optimization-on-schaffer-n-4-function.md>): Mathematical Optimization; Schaffer N. 4 function; Schaffer N. 4 Minima: 0.500009.

- [Mathematical Optimization on Rosenbrock function n=3](<https://www.sota2.com/research/sota/mathematical-optimization-on-rosenbrock-function-n-3.md>): Mathematical Optimization; Rosenbrock function n=3; Functional Minima: 1.0576.

- [Mathematical Optimization on Ackley function](<https://www.sota2.com/research/sota/mathematical-optimization-on-ackley-function.md>): Mathematical Optimization; Ackley function; Functional Minima: 0.008.

- [Mathematical Optimization on Three-hump camel function](<https://www.sota2.com/research/sota/mathematical-optimization-on-three-hump-camel-function.md>): Mathematical Optimization; Three-hump camel function; Functional Minima: 0.000004.

- [Mathematical Optimization on GoldsteinPrice function](<https://www.sota2.com/research/sota/mathematical-optimization-on-goldsteinprice-function.md>): Mathematical Optimization; GoldsteinPrice function; Functional Minima: 3.0022.

- [Mathematical Optimization on McCormick function](<https://www.sota2.com/research/sota/mathematical-optimization-on-mccormick-function.md>): Mathematical Optimization; McCormick function; Functional Minima: -1.9132.

- [Mathematical Optimization on Bukin N.6 function](<https://www.sota2.com/research/sota/mathematical-optimization-on-bukin-n-6-function.md>): Mathematical Optimization; Bukin N.6 function; Bukin N.6 Functional Minima: 0.2921.

- [Mathematical Optimization on Lévi N.13](<https://www.sota2.com/research/sota/mathematical-optimization-on-l-vi-n-13.md>): Mathematical Optimization; Lévi N.13; Functional Minima: 0.0015.

- [Mathematical Optimization on Easom function](<https://www.sota2.com/research/sota/mathematical-optimization-on-easom-function.md>): Mathematical Optimization; Easom function; Functional Minima: -0.0089.

- [Mathematical Optimization on Cross-in-tray](<https://www.sota2.com/research/sota/mathematical-optimization-on-cross-in-tray.md>): Mathematical Optimization; Cross-in-tray; Functional Minima: -2.06261.

- [Mathematical Optimization on Holder table function](<https://www.sota2.com/research/sota/mathematical-optimization-on-holder-table-function.md>): Mathematical Optimization; Holder table function; Functional Minima: -19.2073.

- [Mathematical Optimization on Styblinski Tang function (n=2)](<https://www.sota2.com/research/sota/mathematical-optimization-on-styblinski-tang-function-n-2.md>): Mathematical Optimization; Styblinski Tang function (n=2); Functional Minima: -78.331.

- [Mathematical Optimization on Sphere function n=10](<https://www.sota2.com/research/sota/mathematical-optimization-on-sphere-function-n-10.md>): Mathematical Optimization; Sphere function n=10; Functional Minima: 0.0111.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1506.08004>)

[PDF](<https://arxiv.org/pdf/1506.08004.pdf>)