An Improved Model of Ant Colony Optimization Using a Novel Pheromone Update Strategy

The paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics. key words: Ant colony optimization, ant colony system, ant-miner, classification rule mining, learning automata, reinforcement learning

[1]  Pooia Lalbakhsh,et al.  Focusing on rule quality and pheromone evaporation to improve ACO rule mining , 2011, 2011 IEEE Symposium on Computers & Informatics.

[2]  M. Dorigo,et al.  1 Positive Feedback as a Search Strategy , 1991 .

[3]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Marco Dorigo,et al.  Ant algorithms and stigmergy , 2000, Future Gener. Comput. Syst..

[5]  B. Bullnheimer,et al.  A NEW RANK BASED VERSION OF THE ANT SYSTEM: A COMPUTATIONAL STUDY , 1997 .

[6]  J. K. Lenstra,et al.  Local Search in Combinatorial Optimisation. , 1997 .

[7]  Marco Dorigo,et al.  Ant colony optimization and its application to adaptive routing in telecommunication networks , 2004 .

[8]  Bo Liu,et al.  Density-Based Heuristic for Rule Discovery with Ant-Miner , 2002 .

[9]  M Dorigo,et al.  Ant colonies for the travelling salesman problem. , 1997, Bio Systems.

[10]  Marco Dorigo,et al.  AntNet: Distributed Stigmergetic Control for Communications Networks , 1998, J. Artif. Intell. Res..

[11]  Francisco Herrera,et al.  A New ACO Model Integrating Evolutionary Computation Concepts: The Best-Worst Ant System , 2000 .

[12]  TsaiChun-Wei,et al.  A new hybrid heuristic approach for solving large traveling salesman problem , 2004 .

[13]  Yves Crama,et al.  Local Search in Combinatorial Optimization , 2018, Artificial Neural Networks.

[14]  Ann Nowé,et al.  Colonies of learning automata , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[15]  Kumpati S. Narendra,et al.  Learning automata - an introduction , 1989 .

[16]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[17]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[18]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[19]  Luca Maria Gambardella,et al.  AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks , 2005, Eur. Trans. Telecommun..

[20]  T. Stützle,et al.  MAX-MIN Ant System and local search for the traveling salesman problem , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[21]  Hussein A. Abbass,et al.  Classification rule discovery with ant colony optimization , 2003, IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT 2003..

[22]  Alex Alves Freitas,et al.  Data mining with an ant colony optimization algorithm , 2002, IEEE Trans. Evol. Comput..

[23]  M. Dorigo,et al.  Ant System: An Autocatalytic Optimizing Process , 1991 .

[24]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[25]  B. John Oommen,et al.  Cybernetics and Learning Automata , 2009, Handbook of Automation.