SWARM OPTIMIZATION ALGORITHM BASED ON THE ANT COLONY LIFE CYCLE

Abstract

Optimization is very important to the success of any business. One technique for solving optimization is swarm intelligence; it has been successfully applied to solve a wide range of optimization problems. We devised a new swarm intelligence optimization algorithm based on the cooperative behavior of three different kinds of ants in a colony. Our algorithm consists of both exploration and exploitation processes to achieve better search performance. A new local search, inspired by the foraging of desert ants, was introduced to help the search move away from the local optima. Performance was evaluated on 23 standard benchmark functions of varying complexity. Our algorithm was able to find the global optima in more than 80 percent of the test functions, whereas the second-place algorithm only found around 10 percent of the functions tested.