A New Optimization Approach Based on Rotational Mutation and Crossover Operator

TL;DR

This paper introduces RMCGA, a new optimization method combining rotational mutation and crossover, which efficiently finds global optima in continuous functions with fewer generations compared to existing algorithms.

Contribution

The paper presents a novel optimization approach (RMC) that integrates rotational mutation and crossover, improving convergence speed and accuracy in global optimization tasks.

Findings

RMCGA achieves global optimal points with fewer generations.

RMCGA outperforms DE, PGA, Grefensstette, and Eshelman algorithms.

Numerical and simulation results validate the effectiveness of RMCGA.

Abstract

Evaluating a global optimal point in many global optimization problems in large space is required to more calculations. In this paper, there is presented a new approach for the continuous functions optimization with rotational mutation and crossover operator. This proposed method (RMC) starts from the point which has best fitness value by elitism mechanism and after that rotational mutation and crossover operator are used to reach optimal point. RMC method is implemented by GA (Briefly RMCGA) and is compared with other wellknown algorithms such as: DE, PGA, Grefensstette and Eshelman[15,16] and numerical and simulating results show that RMCGA achieve global optimal point with more decision by smaller generations.