Ant colony pattern search algorithms and their convergence
Zuren Feng · Control theory & applications · 2007
A class of ant colony pattern search algorithms (ACPSAs) are designed for the optimization of multimodal functions in continuous space. ACPSAs guide the individuals to perform region searches by objective function heuristic pheromone. Further local searches are handled by pattern searches of individuals, then the search results are shared with pheromone fusion, providing the basis for the region searches in the next iteration. The probabilistic convergence theories of ACPSAs are also given by stochastic pattern search algorithm theory. APCSAs present interesting emergent properties as shown by some analytical test functions. Finally, the comparison results with typical stochastic optimization algorithms show the effectiveness of the algorithms and the advantage in swarm cooperation.