Rotational Mutation Genetic Algorithm on optimization Problems

TL;DR

This paper introduces a rotational mutation genetic algorithm (RMGA) for optimizing continuous functions, demonstrating improved efficiency and accuracy over existing algorithms through numerical and simulation results.

Contribution

The paper presents a novel rotational mutation approach integrated with genetic algorithms, enhancing global optimization performance in continuous spaces.

Findings

RMGA achieves global optima with fewer generations

RMGA outperforms DE, PGA, Grefensstette, and Eshelman algorithms

Numerical and simulation results validate RMGA's effectiveness

Abstract

Optimization problem, nowadays, have more application in all major but they have problem in computation. Calculation of the optimum point in the spaces with the above dimensions is very time consuming. In this paper, there is presented a new approach for the optimization of continuous functions with rotational mutation that is called RM. The proposed algorithm starts from the point which has best fitness value by elitism mechanism. Then, method of rotational mutation is used to reach optimal point. In this paper, RM algorithm is implemented by GA(Briefly RMGA) and is compared with other well- known algorithms: DE, PGA, Grefensstette and Eshelman [15, 16] and numerical and simulation results show that RMGA achieve global optimal point with more decision by smaller generations.