Curved Space Optimization: A Random Search based on General Relativity Theory
This addresses the open problem of global optimization for researchers, but it appears incremental as it builds on random search with a novel theoretical twist.
The authors tackled the problem of designing a fast and efficient global optimization method with local optima avoidance by introducing Curved Space Optimization (CSO), a probabilistic method enhanced by concepts from general relativity theory, and results showed promising performance on unimodal and multimodal benchmark functions across different search space dimensions.
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.