Sub- Diving Labeling Method for Optimization Problem by Genetic Algorithm

TL;DR

This paper introduces a novel subdivision labeling genetic algorithm (SLMGA) for high-dimensional global optimization, reducing computational effort and improving convergence to the global optimum.

Contribution

It proposes a new hybrid algorithm combining subdivision labeling with genetic algorithms to enhance efficiency in high-dimensional optimization problems.

Findings

RSLMGA achieves global optimal points with fewer generations.

The proposed method outperforms DE, PGA, Grefensstette, and Eshelman algorithms.

Numerical results demonstrate improved decision accuracy.

Abstract

In many global Optimization Problems, it is required to evaluate a global point (min or max) in large space that calculation effort is very high. In this paper is presented new approach for optimization problem with subdivision labeling method (SLM) but in this method for higher dimensional has high computational. SLM Genetic Algorithm (SLMGA) in optimization problems is one of the solutions of this problem. In proposed algorithm the initial population is crossing points and subdividing in each step is according to mutation. RSLMGA is compared with other well known algorithms: DE, PGA, Grefensstette and Eshelman and numerical results show that RSLMGA achieve global optimal point with more decision by smaller generations.