Effective Parallelization of Stochastic Evolution

Abstract

The paper discusses the parallelization of Stochastic Evolution metaheuristic, identifying effective parallel strategies for a distributed parallel environment. Multiobjective VLSI cell placement is used as the optimization problem. A comprehensive set of parallelization approaches are tested and an effective strategy is identified in terms of two underlying principles: effective workload division and the effect of parallelization on metaheuristic’s search intelligence. The strategies are compared with another similar evolutionary metaheuristic called Simulated Evolution and the interrelation between effective parallelization and algorithm’s intelligence is highlighted using effective parallelization of stochastic evolution, the parallelized version of which has not been presented before.