Improved And Application Of Glowworm Swarm Optimization Algorithm

Glowworm swarm optimization algorithm is from the nature of the foraging behavior of fireflies group, which is in nature with a certain number of fluorescein, and can send information to other fireflies around by the luminous intensity of an individual. Generally, the stronger of the light given out by the fire worm,the higher of the location of the fireflies food concentration,it indicates the location of the fireflies food concentration is higher, and the fireflies will move to the fireflies which is dark. By means of moving and iteration of the fireflies group constantly, finally, most fireflies will converge to the brightest location where fireflies, the location is the optimal solution. Firefly algorithm as a kind of swarm intelligence optimization algorithm, has the advantages of the simple implementation and robust, which already have been applied in many fields, such as, function optimization, combinatorial optimization problems, multiple source localization. Meanwhile, the glowworm swarm optimization algorithm is not very long since its birth till now, so there are many applications need to be extended. In this paper, it used the improved glowworm swarm optimization algorithm to solve the two types of similar machine scheduling problem, which extended its application field. On the one hand,the improved algorithm learned from the mutation operator and selection operator of genetic algorithm to strengthen the global exploring ability of the algorithm; On the other hand, the improved algorithm joined the mountain climbing algorithm as a local search algorithm, to strengthen the original local exploring ability of the algorithm. Test results show that the proposed algorithm has better global convergence and higher optimization efficiency, warm intelligence optimization strategy proposed can effectively solve two kinds of similar machine scheduling problem.