Combining Genetic Algorithms And Motor Nest Optimization To Solve The Supplier's Selection Problem

The genetic algorithm was invented in 1980 based on Darwin's evolutionary theory to solve optimization problems. In fact, the genetic algorithm is based on the principle of "continuing the life of the best" and "proliferation of the superior type". The main operators of the genetic algorithm include: coding, selection, integration operator and mutation operator. The performance of the genetic algorithm is very good in the first few repetitions, but with the continuation of the trend and the increase in the number of repetitions, we will face a multitude of excessive results and results, which increases the number of repetitions to the optimal response. The method of optimizing the ant is derived from the actual motion of the ant in nature to find food, which was first introduced in Marco Dorigo's doctoral dissertation in 1992 as the ant system. The algorithm introduced by Dorigo is based on two basic principles: 1- Injection and evaporation of the pheromone 2- potential tendency of the ant into the pheromone. The algorithm optimization of the ant nest has a slow convergence rate due to the absence of pheromones in the early repetitions. But with the continuation of the search process and the increase in the concentration of the pumped porose, it works very well in the final stages. The combined algorithm with the combination of two genetic algorithms and the curry optimization of the ant uses the benefits of both algorithms. In the hybrid algorithm, we first use a few repetitions of genetic algorithm, then consider the chromosome from the genetic algorithm as the primary answer of the ant algorithm, and continue the subsequent repetitions until the optimal answer with the ant algorithm. The combination algorithm performs better in terms of convergence and efficiency, better than genetic algorithms and ant nest optimization.