Rotational Mutation Genetic Algorithm on optimization Problems
This work addresses optimization problems in various fields by improving computational efficiency, though it appears incremental as it builds on existing genetic algorithm methods.
The paper tackles the computational challenge of finding optimal points in high-dimensional spaces by introducing a rotational mutation genetic algorithm (RMGA). The results show that RMGA achieves the global optimum with fewer generations compared to existing algorithms like DE, PGA, Grefenstette, and Eshelman.
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.