New Optimization Approach Using Clustering-Based Parallel Genetic Algorithm

TL;DR

This paper introduces a clustering-based parallel genetic algorithm to improve the efficiency of high-dimensional global optimization problems, demonstrating enhanced speed and effectiveness over traditional methods.

Contribution

The paper proposes a novel parallel genetic algorithm integrated with subdivision labeling for high-dimensional optimization, reducing computational effort and increasing accuracy.

Findings

Improved speed up and efficiency in optimization tasks.

Effective handling of high-dimensional problems.

Enhanced performance over existing methods.

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 calculation effort. Clustering-Based Parallel Genetic Algorithm (CBPGA) in optimization problems is one of the solutions of this problem. That the initial population is crossing points and subdividing in each step is according to mutation. After labeling all of crossing points, selecting is according to polytope that has complete label. In this method we propose an algorithm, based on parallelization scheme using master-slave. SLM algorithm is implemented by CBPGA and compared the experimental results. The numerical examples and numerical results show that SLMCBPGA is improved speed up and efficiency.