Performance analysis of synchronous and asynchronous distributed genetic algorithms on multiprocessors

Because of their effectiveness and flexibility in finding useful solutions, Genetic Algorithms (GM) are very popular search techniques for solving complex optimization problems in scientific and industrial fields. Parallel GM (PGAs), and especially distributed ones have been usually presented as the way to overcome the time-consuming shortcoming of sequential GM. In the case of applying PGAs, we can expect better performance, the reason being the exchange of knowledge during the parallel search process. The resulting distributed search is different compared to what sequential pansnictic GM do, then deserving additional studies. This article presents a performance study of three different PGAs. Moreover, we investigate the effect of synchronizing communications over modern shared-memory multiprocessors. We consider the master-slave model along with synchronous and asynchronous distributed GM (dGAs), presenting their different designs and expected similarities when running in a number of cores ranging from one to 32 cores. The master-slave model showed a competitive numerical effort versus the other dGAs and demonstrated to be able to scale-up well over multiprocessors. We describe how the speed-up and parallel performance of the dGAs is changing as the number of cores enlarges. Results of the island model show that synchronous and asynchronous dGAs have different numerical performances on a multiprocessor, the asynchronous algorithm having a faster execution, thus more attractive for time demanding applications. Our results and statistical analyses help in developing a novel body of knowledge on PGAs running in shared memory multiprocessors (versus overwhelming literature oriented to distributed memory clusters), something useful for researchers, beginners, and final users of these techniques.