Sub- Diving Labeling Method for Optimization Problem by Genetic Algorithm
This addresses computational efficiency in optimization for researchers and practitioners, but it is incremental as it builds on existing genetic algorithms and subdivision methods.
The paper tackles the high computational cost of subdivision labeling methods in high-dimensional global optimization by introducing SLMGA, a genetic algorithm variant that uses crossing points and mutation-based subdivision. Numerical results show that RSLMGA achieves the global optimum with fewer generations compared to algorithms like DE and PGA.
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.