An ant colony optimization heuristic for constrained task allocation problem

Abstract I present an ant colony optimization (ACO) heuristic for solving the constraint task allocation problem (CTAP). Using real-world and simulated datasets, I compare the results of ACO with those of mixed integer programming (MIP) formulation, iterated greedy (IG) heuristic, and tighter of linear programming or Lagrangian relaxation based lower bounds. For datasets where optimal results could be obtained using the MIP formulation, the ACO results were either optimal or very tight with an average relative gap of less than 0.5% from the optimal value. When comparing the ACO results to the best lower bound, the ACO results had an average relative gap of approximately 3%. In all cases, the ACO algorithm found better results than the IG heuristic. The results from my experiments indicate that the proposed ACO heuristic is very promising for solving CTAPs.

[1]  Peng-Yeng Yin,et al.  Multi-objective task allocation in distributed computing systems by hybrid particle swarm optimization , 2007, Appl. Math. Comput..

[2]  Syam Menon.  Effective reformulations for task allocation in distributed systems with a large number of communicating tasks , 2004, IEEE Transactions on Knowledge and Data Engineering.

[3]  John K. Davis,et al.  Real-time task allocation for remote weapon operators , 2013, Proceedings of the 16th International Conference on Information Fusion.

[4]  Philip Wolfe,et al.  Validation of subgradient optimization , 1974, Math. Program..

[5]  Peng-Yeng Yin,et al.  A Cyber Swarm Algorithm for Constrained Program Module Allocation Problem , 2013, KSE.

[6]  Alain Billionnet,et al.  An efficient algorithm for a task allocation problem , 1992, JACM.

[7]  C. Siva Ram Murthy,et al.  Task Allocation Algorithms for Maximizing Reliability of Distributed Computing Systems , 1997, IEEE Trans. Computers.

[8]  Yskandar Hamam,et al.  Assignment of program modules to processors: A simulated annealing approach , 2000, Eur. J. Oper. Res..

[9]  Wun-Hwa Chen,et al.  A hybrid heuristic to solve a task allocation problem , 2000, Comput. Oper. Res..

[10]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[11]  Harold S. Stone,et al.  Assignment of Tasks in a Distributed Processor System with Limited Memory , 1979, IEEE Transactions on Computers.

[12]  H. Sherali,et al.  On the choice of step size in subgradient optimization , 1981 .

[13]  Katta G. Murty,et al.  A hybrid genetic/optimization algorithm for a task allocation problem , 1999 .

[14]  Shih-Wei Lin,et al.  Multiprocessor task scheduling in multistage hybrid flowshops: A hybrid artificial bee colony algorithm with bi-directional planning , 2013, Comput. Oper. Res..

[15]  Jun Wei,et al.  An effective iterated greedy algorithm for reliability-oriented task allocation in distributed computing systems , 2013, J. Parallel Distributed Comput..

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

[17]  Franz Rendl,et al.  Solving Graph Bisection Problems with Semidefinite Programming , 2000, INFORMS J. Comput..

[18]  Andreas T. Ernst,et al.  Exact Solutions to Task Allocation Problems , 2006, Manag. Sci..

[19]  Jean-Louis Goffin,et al.  On convergence rates of subgradient optimization methods , 1977, Math. Program..

[20]  Parag C. Pendharkar,et al.  A multi-agent memetic algorithm approach for distributed object allocation , 2011, J. Comput. Sci..

[21]  William J. B. Oldham,et al.  Dynamic Task Allocation Models for Large Distributed Computing Systems , 1995, IEEE Trans. Parallel Distributed Syst..

[22]  Viktor K. Prasanna,et al.  Adaptive Allocation of Independent Tasks to Maximize Throughput , 2007, IEEE Transactions on Parallel and Distributed Systems.

[23]  Siti Sakira Kamaruddin,et al.  Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting , 2014, J. Comput. Sci..

[24]  Amir Hossein Gandomi,et al.  Chaotic bat algorithm , 2014, J. Comput. Sci..

[25]  M. Patriksson,et al.  Conditional subgradient optimization -- Theory and applications , 1996 .