Skip to main content

Advertisement

Log in

A Heuristic Genetic Algorithm for Solving Resource Allocation Problems

  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract.

In the paper, a heuristic genetic algorithm for solving resource allocation problems is proposed. The resource allocation problems are to allocate resources to activities so that the fitness becomes as optimal as possible. The objective of this paper is to develop an efficient algorithm to solve resource allocation problems encountered in practice. Various genetic algorithms are studied and a heuristic genetic algorithm is proposed to ameliorate the rate of convergence for resource allocation problems. Simulation results show that the proposed algorithm gives the best performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bäck T, Hammel U, Schwefel HP (1997) Evolutionary computation: comments on the history and current state. IEEE Transactions on Evolutionary Computation 1(1): 1–17

    Google Scholar 

  2. Bjorndal AMH, Caprara A, Cowling PI et al (1995) Some thoughts on combinatorial optimisation. European Journal of Operational Research 83: 253–270

    Google Scholar 

  3. Gen Mitsuo and Cheng Runwei (1997) Genetic algorithms, engineering design. Wiley, New York, pp 1–2

  4. Goldberg DA (1989) Genetic algorithms in search, optimization, and machine learning. Adision-Wesley, Reading, MA, pp 170–174

  5. Hammer PL, Hansen P, Simeone B (1984) Roof duality, complementation and persistency in quadratic 0-1 optimization. Mathematical Programming 28: 121–155

    Google Scholar 

  6. Ibarraki Toshihide and Katohi Naoki (1988) Resource allocation problems. MIT Press, Cambridge, MA, pp 1–10

  7. Kolen A, Pesch E (1994) Genetic local search in combinatorial optimization. Discrete Applied Mathematics and Combinatorial Operations Research and Computer Science 48: 273–284

    Google Scholar 

  8. Lee ZJ, Lee CY, Su SF (2000) A fuzzy-genetic based decision aided system for naval weapon–target assignment problems. Proceedings of 2000 ROC automatic control conference 1: 163–168

  9. Lin FT, Kao CY, Hsu CC (1993) Applying the genetic approach to simulated annealing in solving some NP-hard problems. IEEE Transactions on Systems, Man and Cybernetics 23(6): 1752–1767

    Google Scholar 

  10. Lloyd SP, Witsenhausen HS (1986) Weapon allocation is NP-complete. IEEE summer simulation conference, Reno, Nevada

  11. Merz P, Freisleben B (2000) Fitness landscape analysis and memetic algorithms for quadratic assignment problem. IEEE Transactions on Evolutionary Computation 4(4): 337–352

    Google Scholar 

  12. Olsen A (1994) Penalty functions and the knapsack problem. Proceedings of the first IEEE conference on evolutionary computation 6: 554–558

  13. Sahni S, Gonzales T (1976) P-complete approximation problem. ACM Journal 23: 556–565

    Google Scholar 

  14. Tate DM, Smith AE (1995) A genetic approach to the quadratic assignment problem. Computers Ops. Res. 22(1): 73–83

    Google Scholar 

  15. Van Laarhoven PJM, Arts EHL (1992) Simulated annealing: theory and applications. Kluwer Academic, Dordrecht, pp 9–12

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shun-Feng Su.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, ZJ., Su, SF., Lee, CY. et al. A Heuristic Genetic Algorithm for Solving Resource Allocation Problems. Knowledge and Information Systems 5, 503–511 (2003). https://doi.org/10.1007/s10115-003-0082-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-003-0082-0

Keywords

Navigation