TY - GEN

T1 - Differential evolution algorithms for the generalized assignment problem

AU - Tasgetiren, M. Fatih

AU - Suganthan, P. N.

AU - Chua, Tay Jin

AU - Al-Hajri, Abdullah

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=70450092380&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70450092380&partnerID=8YFLogxK

U2 - 10.1109/CEC.2009.4983269

DO - 10.1109/CEC.2009.4983269

M3 - Conference contribution

AN - SCOPUS:70450092380

SN - 9781424429592

T3 - 2009 IEEE Congress on Evolutionary Computation, CEC 2009

SP - 2606

EP - 2613

BT - 2009 IEEE Congress on Evolutionary Computation, CEC 2009

T2 - 2009 IEEE Congress on Evolutionary Computation, CEC 2009

Y2 - 18 May 2009 through 21 May 2009

ER -