Monkey King Immune Evolutionary Algorithm

Abstract

Monkey-King genetic algorithm has the shortages of the lower searching ability in the local area and further in the whole area at monkey-king point in spite of the advantages of the simple principle and easy calculation. Monkey-king point was optimized iteratively by using immune evolutionary algorithm. This method overcomes the premature convergence because of the optimal searching in the out as well as in of the monkey-king point. At the same time, with the lapse of iteration, the algorithm closes in the whole of optimal solution with the higher precision because of the gradual strengthening of local searching ability. Calculation and comparison with several methods, such as monkey-king genetic algorithm, improved monkey-king genetic algorithm and common climbing operator genetic algorithm et al, has made. The results show that the monkey-king immune evolutionary algorithm has the optimal searching ability and the stability all the better.