Improving Search by Incorporating Evolution Principles in Parallel Tabu Search

Combinatorial optimization problems require computing efforts which grow at least exponentially with the problem dimension. Therefore, the use of the remarkable power of massively parallel systems constitutes an opportunity to be considered for solving significant applications in reasonable times. In this paper, starting from Tabu Search, a general optimization methodology, a parallel version, oriented to distributed memory multiprocessors and including evolution principles, has been introduced and discussed. The experiments have been performed on classical Travelling Salesman Problems and Quadratic Assignment Problems taken from literature. The results obtained show that the incorporation of evolution principles is very fruitful for the search strategy in terms of both convergence speed and solution precision.