An Accelerated Ant Colony Algorithm for Complex Nonlinear System Optimization

Abstract

Ant colony algorithms as a category of evolutionary computational intelligence can deal with complex optimization problems better than traditional optimization techniques. An accelerated ant colony algorithm is proposed in this paper to tackle complex nonlinear system optimization problems by using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be obtained more efficiently through self-adjusting the path searching behaviors of the artificial ants. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The simulation results convectively show that, in comparison with traditional optimization approaches and currently used basic ant colony algorithms, the proposed algorithm possess prominent capability in dealing with complex nonlinear system optimization problems with extremely complex solution structures and is applicable in solving complicated nonlinear optimization problems in practice such as network optimization and transportation problems.