Differential evolution algorithms for the generalized assignment problem

M. Fatih Tasgetiren, P. N. Suganthan, Tay Jin Chua, Abdullah Al-Hajri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Citations (Scopus)


In this paper, differential evolution (DE) algorithms are presented to solve the generalized assignment problem (GAP), which is basically concerned with finding the minimum cost assignment of jobs to agents such that each job is assigned to exactly one agent, subject to capacity constraint of agents. The first algorithm is unique in terms of solving a discrete optimization problem on a continuous domain. Thesecond one is a discrete/combinatorial variant of the traditional differential evolution algorithm working on a discrete domain. The objective is to present a continuous optimization algorithm dealing with discrete spaces hence to solve a discrete optimization problem. Both algorithms are hybridized with a "blind" variable neighborhood search (VNS) algorithm tofurther enhance the solution quality, especially to end up with feasible solutions. They are tested on a benchmark suite from OR Library. Computational results are promising for acontinuous algorithm such that without employing any problem-specific heuristics and speed-up methods, the DE variant hybridized with a "blind" VNS local search was able togenerate competitive results to its discrete counterpart.

Original languageEnglish
Title of host publication2009 IEEE Congress on Evolutionary Computation, CEC 2009
Number of pages8
Publication statusPublished - 2009
Event2009 IEEE Congress on Evolutionary Computation, CEC 2009 - Trondheim, Norway
Duration: May 18 2009May 21 2009


Other2009 IEEE Congress on Evolutionary Computation, CEC 2009

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science


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