An efficient discrete particle swarm algorithm for Task Assignment Problems

Task Assignment Problems (TAPs) in distributed computer system are general NP-hard and usually modeled as integer programming discrete problems. Many algorithms are proposed to resolve those problems. Discrete particle swarm algorithm (DPS) is a newly developed method to solve constraint satisfaction problem (CSP) which has advantage on search capacity and can find more solutions. We proposed an improved DPS to solve TAP in this paper. DPS has a special operator namely coefficient multiplying speed, which is designed for CSP but does not exist in other discrete problems. Thus we redefined a coefficient multiplying speed operator with probability selection. We analyzed the speed and position updating formula then we derived a refined position updating formula. Several experiments are carried out to test our DPS. Experimental results show that our algorithm has more efficient search capacity, higher success rate, less running time and more robust.

[1]  C. Ribeiro,et al.  A Tabu Search Approach to Task Scheduling on Heterogeneous Processors under Precedence Constraints , 1995, Int. J. High Speed Comput..

[2]  Hesham El-Rewini,et al.  Scheduling Parallel Program Tasks onto Arbitrary Target Machines , 1990, J. Parallel Distributed Comput..

[3]  B. Naudts,et al.  Swarm intelligence on the binary constraint satisfaction problem , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[4]  Ching-Chi Hsu,et al.  Task assignment scheduling by simulated annealing , 1990, IEEE TENCON'90: 1990 IEEE Region 10 Conference on Computer and Communication Systems. Conference Proceedings.

[5]  Jake K. Aggarwal,et al.  A Generalized Scheme for Mapping Parallel Algorithms , 1993, IEEE Trans. Parallel Distributed Syst..

[6]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[7]  Russell C. Eberhart,et al.  Solving Constrained Nonlinear Optimization Problems with Particle Swarm Optimization , 2002 .

[8]  Harold S. Stone,et al.  Multiprocessor Scheduling with the Aid of Network Flow Algorithms , 1977, IEEE Transactions on Software Engineering.

[9]  Donald F. Towsley.  Allocating programs containing branches and loops within a multiple processor system , 1986, IEEE Transactions on Software Engineering.

[10]  Ce-Kuen Shieh,et al.  A Hopfield neural network based task mapping method , 1999, Comput. Commun..

[11]  Xiao-Feng Xie,et al.  DEPSO: hybrid particle swarm with differential evolution operator , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[12]  Tarek A. El-Ghazawi,et al.  A Parallel Genetic Algorithm for Task Mapping on Parallel Machines , 1999, IPPS/SPDP Workshops.

[13]  Sung-Bong Yang,et al.  Task scheduling in distributed computing systems with a genetic algorithm , 1997, Proceedings High Performance Computing on the Information Superhighway. HPC Asia '97.

[14]  Daniel P. Siewiorek,et al.  Simulated annealing applied to multicomputer task allocation and processor specification , 1996, Proceedings of SPDP '96: 8th IEEE Symposium on Parallel and Distributed Processing.

[15]  Shahid H. Bokhari,et al.  A Shortest Tree Algorithm for Optimal Assignments Across Space and Time in a Distributed Processor System , 1981, IEEE Transactions on Software Engineering.

[16]  Ioannis Milis.  Task Assignment in Distributed Systems Using Network Flow Methods , 1995, Combinatorics and Computer Science.

[17]  Imtiaz Ahmad,et al.  Particle swarm optimization for task assignment problem , 2002, Microprocess. Microsystems.

[18]  Donald F. Towsley,et al.  Allocating programs containing branches and loops within a multiple processor system , 1986, IEEE Transactions on Software Engineering.

[19]  J.E. Cooling,et al.  Task scheduling-using neural networks within hardware co-processors , 1994, Proceedings of MELECON '94. Mediterranean Electrotechnical Conference.