Parallelizing a global optimization method in a distributed-memory environment

We present research into parallelizing the zone-parallel method of global optimization. The method belongs to the class of genetic algorithms and is briefly, described in the paper upon introduction to genetic algorithms, parallelization models for genetic algorithms are presented. The subsequent part of the paper is devoted to the global optimization problem of finding sources of tremors in coal mines. First, a short description of the S-P method for localizing hypocenters of tremors is given; the method requires minimizing the error function for hypocenter location. Next, a practical coal-mining example is given where data on a tremor are collected by seismometers and the location of the the hypocenter is found by employing the zone parallel method. Experimental results are presented which were obtained from implementing both the sequential and parallel versions of the zone-parallel method in a local area network of Sun Ultra workstations. The results show suitability of the island model of parallelization for this optimization method as well as disproving the usefulness of the master-slave model.