Solving TSP with improved multi-Agent genetic algorithm

Based on agent's capability of perceiving and reacting on environment,Multi-Agent Genetic Algorithm(MAGA)was proposed as a new method of function optimization.MAGA had a rapid convergence velocity especially when it optimized super-high dimensional functions.This algorithm was improved properly based on its characteristics:elitist reservation strategy was adopted in neighborhood orthogonal crossover operator,and neighborhood orthogonal crossover operator was introduced into self-learning operator and small mutation probability was adopted to quicken the convergence speed.The results of solving Traveling Salesman Problem(TSP)show that the performance of improved MAGA is enhanced greatly.