Population declining ant colony optimization algorithm and its applications

Population declining ant colony optimization (PDACO) algorithm is proposed and applied to the traveling salesman problem (TSP) and multiuser detection in this paper. Ant colony optimization (ACO) algorithms have already successfully been used in combinatorial optimization, however, as the pheromone accumulates, we may not get a global optimum because it stops searching early. PDACO can enlarge searching range through increasing the initial population of the ant colony, and the population declines in successive iterations. So, the performance of PDACO is superior with the same computational complexity. PDACO is applied to TSP and multiuser detection. Via computer simulations it is shown that PDACO has better performance in solving these two problems than ACO algorithms.

[1]  Sergio Verdú,et al.  Minimum probability of error for asynchronous Gaussian multiple-access channels , 1986, IEEE Trans. Inf. Theory.

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

[3]  Shihua Gong,et al.  Dynamic ant colony optimisation for TSP , 2003 .

[4]  Fred Glover,et al.  Integrating target analysis and tabu search for improved scheduling systems , 1993 .

[5]  A. Ijspeert,et al.  A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems , 2002, Artificial Life.

[6]  S. Moshavi,et al.  Multi-user detection for DS-CDMA communications , 1996, IEEE Commun. Mag..

[7]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[8]  Zhen-Ping Lo,et al.  Optimization of job scheduling on parallel machines by simulated annealing algorithms , 1992 .

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

[10]  Zhilu Wu,et al.  Stochastic Cellular Neural Network for CDMA Multiuser Detection , 2007, ISNN.

[11]  Hossein Miar Naimi,et al.  New robust and efficient ant colony algorithms: Using new interpretation of local updating process , 2009, Expert Syst. Appl..

[12]  Craig K. Rushforth,et al.  A Family of Suboptimum Detectors for Coherent Multiuser Communications , 1990, IEEE J. Sel. Areas Commun..

[13]  Sreeram Ramakrishnan,et al.  A hybrid approach for feature subset selection using neural networks and ant colony optimization , 2007, Expert Syst. Appl..

[14]  Marco Dorigo,et al.  Ant colony optimization , 2006, IEEE Computational Intelligence Magazine.

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

[16]  Elias S. Manolakos,et al.  Hopfield neural network implementation of the optimal CDMA multiuser detector , 1996, IEEE Trans. Neural Networks.

[17]  Luca Maria Gambardella,et al.  A Study of Some Properties of Ant-Q , 1996, PPSN.

[18]  J. Deneubourg,et al.  Self-organized shortcuts in the Argentine ant , 1989, Naturwissenschaften.

[19]  S.L. Hijazi,et al.  Novel low-complexity DS-CDMA multiuser detector based on ant colony optimization , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[20]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

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

[22]  Vittorio Maniezzo,et al.  Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem , 1999, INFORMS J. Comput..

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