Artificial ant colonies

Izrailev, S. and Agrafiotis, D.K. (2001a) A new method for building regression tree models for QSAR based on artificial ant colony systems./. Chem. Inf. Comput. Sci., 41, 176-180. 

Artificial intelligence algorithms have also been embedded in docking codes, notably ant colony optimization (AGO) and particle swarm optimization (PSO) [80,... 

Colorni, A., Dorigo, M., Maniezzo, V. Distributed optimization by ant colonies. In Proceedings of the 1st European Conference on Artificial Life, Cambridge, pp. 

Baykasoglu, A. Ozbakir, L. Tapkan, P. (2007). Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem. In Swarm Intelligence Focus on Ant and Particle Swarm Optimization, F.T.S. Chan and M.K Tiwari (Eds.), Itech Education and Publishing, Vienna, Austria, pp. 113-144. 

Ant Colony Optimization (ACO) meta-heuristics is one such technique that is based on the cooperative forging strategy of real ants [1],[2]. In this approach, several artificial ants perform a sequence of operations iteratively. Ants are guided by a greedy heuristic algorithm which is problem dependenf that aid their search for better solutions iteratively. Ants seek solutions using information gathered previously to... 

The ant algorithm reproduces this behaviour of ant colonies in the pheromone trail update procedure. The search for a solution is terminated, when the maximum number of iterations has been reached or the maximum computational time has been achieved. Then, the shortest trajectoiy is selected from all of the solutions found by the artificial ants. Afterwards, the best solution is presented in a graphical form and the new course of the own ship at every segment of the determined safe, optimal trajectory is displayed. If the algorithm has not found a solution, such information is displayed after the calculations are completed. The flowchart of the ant algorithm for safe ship trajectoiy planning is shown in Figure 1. 

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