An Ant Algorithm for Nonlinear Optimization

摘要

Based on the swarm intelligence of ants, this paper presents a new stochastic searching algorithm for the general (constrained or unconstrained) nonlinear optimization. The proposed algorithm requires no prerequisites of differentiation or even continuation of objective function, and can effectively overcome the commonly seen disadvantage of getting into local optimum by classical algorithms. Series of numerical experiments show the good performance of the algorithm.