Cloud Model Glowworm Swarm Optimization Algorithm for Functions Optimization

摘要

For basic artificial glowworm swarm optimization algorithm has a slow convergence and easy to fall into local optimum, and the cloud model has excellent characteristics with uncertainty knowledge representation, an artificial glowworm swarm optimization algorithm based on cloud model is presented by utilizing these characteristics. The algorithm selects an optimal value of each generation as the center point of the cloud model, compares with cloud droplets and then achieves the better search value of groups which can avoid falling into the local optimum and can speed up the convergence rate of the algorithm. Finally, we use the standard function to test the algorithm. And the test results show that the convergence and the solution accuracy of our proposed algorithm have been greatly improved compared with the basic artificial glowworm swarm optimization algorithm.