Curved Space Optimization: A Random Search based on General Relativity Theory

TL;DR

This paper introduces Curved Space Optimization (CSO), a novel global optimization method inspired by general relativity, which transforms the search space to improve performance and convergence in complex optimization problems.

Contribution

The paper presents a new probabilistic optimization approach that applies space-time curvature concepts from general relativity to enhance global search capabilities.

Findings

CSO performs well on unimodal and multimodal benchmark functions.

The method shows promising results across different search space dimensions.

CSO demonstrates competitive convergence speed.

Abstract

Designing a fast and efficient optimization method with local optima avoidance capability on a variety of optimization problems is still an open problem for many researchers. In this work, the concept of a new global optimization method with an open implementation area is introduced as a Curved Space Optimization (CSO) method, which is a simple probabilistic optimization method enhanced by concepts of general relativity theory. To address global optimization challenges such as performance and convergence, this new method is designed based on transformation of a random search space into a new search space based on concepts of space-time curvature in general relativity theory. In order to evaluate the performance of our proposed method, an implementation of CSO is deployed and its results are compared on benchmark functions with state-of-the art optimization methods. The results show that the performance of CSO is promising on unimodal and multimodal benchmark functions with different search space dimension sizes.