Hardware-oriented ant colony optimization

A new kind of ant colony optimization (ACO) algorithm is proposed that is suitable for an implementation in hardware. The new algorithm - called Counter-based ACO - allows to systolically pipe artificial ants through a grid of processing cells. Various features of this algorithm have been designed so that it can be mapped easily to field-programmable gate arrays (FPGAs). Examples are a new encoding of pheromone information and a new method to define the decision sequence of ants. Experimental results that are based on simulations for the traveling salesperson problem and the quadratic assignment problem are presented to evaluate the proposed techniques.

[1]  Marco Dorigo,et al.  The hyper-cube framework for ant colony optimization , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Franz Rendl,et al.  QAPLIB – A Quadratic Assignment Problem Library , 1997, J. Glob. Optim..

[3]  Maya Gokhale,et al.  Reconfigurable Computing: Accelerating Computation with Field-Programmable Gate Arrays , 2005 .

[4]  Marco Dorigo,et al.  The ant colony optimization meta-heuristic , 1999 .

[5]  Prabhas Chongstitvatana,et al.  A hardware implementation of the Compact Genetic Algorithm , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[6]  Martin Middendorf,et al.  A Population Based Approach for ACO , 2002, EvoWorkshops.

[7]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[8]  Daniel Merkle,et al.  Parallel Ant Colony Algorithms , 2005 .

[9]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[10]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

[11]  Graham M. Megson,et al.  The systolic array genetic algorithm, an example of systolic arrays as a reconfigurable design methodology , 1998, Proceedings. IEEE Symposium on FPGAs for Custom Computing Machines (Cat. No.98TB100251).

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

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

[14]  Marco Dorigo,et al.  HC-ACO: The Hyper-Cube Framework for Ant Colony Optimization , 2001 .

[15]  Stephen M. Trimberger Field-Programmable Gate Array Technology , 2007 .

[16]  Daniel Merkle,et al.  Fast Ant Colony Optimization on Runtime Reconfigurable Processor Arrays , 2002, Genetic Programming and Evolvable Machines.

[17]  Bernd Scheuermann,et al.  FPGA implementation of population-based ant colony optimization , 2004, Appl. Soft Comput..

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

[19]  Martin Middendorf,et al.  Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP , 2001, EvoWorkshops.

[20]  Gerhard Reinelt,et al.  TSPLIB - A Traveling Salesman Problem Library , 1991, INFORMS J. Comput..

[21]  Griffin Caprio,et al.  Parallel Metaheuristics , 2008, IEEE Distributed Systems Online.

[22]  Daniel Merkle,et al.  Ant colony optimization and its application to adaptive routing in telecommunication networks , 2004 .

[23]  Daniel Merkle,et al.  A New Approach to Solve Permutation Scheduling Problems with Ant Colony Optimization , 2001, EvoWorkshops.

[24]  Andrew Lewis,et al.  A Parallel Implementation of Ant Colony Optimization , 2002, J. Parallel Distributed Comput..

[25]  J. McCaskill,et al.  Parallel random number generator for inexpensive configurable hardware cells , 2001 .

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

[27]  Edusmildo Orozco,et al.  Reconfigurable Computing. Accelerating Computation with Field-Programmable Gate Arrays , 2007, Scalable Comput. Pract. Exp..

[28]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .