Bionic Intelligent Optimization Algorithm based on MMAS and Fish-Swarm Algorithm

With large number of ants, the ant colony algorithm would always take a long time or is rather difficult to find the optimal path from complex chapter path, further more, there exists a contradiction between stagnation, accelerated convergence and precocity. In this paper, we propose a new bionic optimization algorithm. The main idea of the algorithm is to introduce the horizons concept in the MMAS fish swarm algorithm, so it would take shorter time to find the optimal path with numerous ants, and the introduction of the concept of fish swarm algorithm congestion level would enable the ant colony find the path of global optimization with a strong crowding limit which avoids the emergence of local extreme and improves the accuracy and efficiency of the algorithm.