Abstract -In this study, we introduce a cooperative parallel tabu search algorithm (CPTS) for the quadratic assignment problem (QAP). The QAP is an NP-hard combinatorial optimization problem that is widely acknowledged to be computationally demanding. These characteristics make the QAP an ideal candidate for parallel solution techniques. CPTS is a cooperative parallel algorithm in which the processors exchange information throughout the run of the algorithm as opposed to independent concurrent search strategies that aggregate data only at the end of execution. CPTS accomplishes this cooperation by maintaining a global reference set which uses the information exchange to promote both intensification and strategic diversification in a parallel environment. This study demonstrates the benefits that may be obtained from parallel computing in terms of solution quality, computational time and algorithmic flexibility. A set of 41 test problems obtained from QAPLIB were used to analyze the quality of the CPTS algorithm. The CPTS algorithm is shown to provide excellent solution quality for all problems in reasonable computational times. Out of 41 test instances CPTS is shown to meet or exceed the average solution quality of some of the best sequential and parallel approaches from the literature on all but 6 problems.
The same set of instances is used by the authors in a different work REF to analyze the impact of a Multi-Start Tabu Search mechanism in terms of diversification and exploitation.
A Cooperative Parallel Tabu Search Algorithm for the Quadratic Assignment Problem
Hybrid Metaheuristics
Mathematics Computer Science