Parallel Information Algorithm with Local Tuning for Solving Multidimensional GO Problems

TL;DR

This paper introduces a parallel global optimization algorithm that combines local tuning and parallel computation, demonstrating significant acceleration in solving multidimensional GO problems through theoretical analysis and numerical experiments.

Contribution

The paper presents a novel parallel algorithm integrating local tuning for multidimensional GO problems, with proven convergence and improved efficiency over existing methods.

Findings

The algorithm converges under specified conditions.

Numerical tests show accelerated performance.

Significant reduction in computation time.

Abstract

In this paper we propose a new parallel algorithm for solving global optimization (GO) multidimensional problems. The method unifies two powerful approaches for accelerating the search: parallel computations and local tuning on the behavior of the objective function. We establish convergence conditions for the algorithm and theoretically show that the usage of local information during the global search permits to accelerate solving the problem significantly. Results of numerical experiments executed with 100 test functions are also reported.