Parallel tabu search versus parallel evolution strategies

Abstract

There exists in scientific, industrial and financial communities a very strong request for techniques able to efficiently solve complex optimization problems. Because of this, several techniques are being currently investigated. Among them evolutionary algorithms and tabu search seem very interesting, not only for their intrinsic features but also because they both are easily parallelizable, so that they can take advantage of the parallel machines available on the market. A new parallel approach to tabu search (PTS) is introduced and compared against parallel evolution strategies on classical optimization problems taken from literature. The experimental results have shown the superiority of the PTS in both the solution quality and the convergence time.<>