Hybrid parallel, evolutionary algorithms for constrained optimization utilizing PC clustering

This paper proposes a hybrid parallelization of evolutionary algorithms (EAs) utilizing PC clustering environments to solve constrained numerical optimization problems. In the proposed parallel structure, the coarse-grained parallel EAs (PEAs) were implicated in upper level and the fine-grained PEAs were used in lower level. The design of effective evolutionary algorithms (EAs) is to obtain a proper balance between exploration and exploitation. The balance can be controlled by the spread rate and the migration of the best individuals. In the hybrid structure, the spread rate is high in lower level coarse-grained structure and low in upper level globally structure. The diversity is promoted by dividing individuals to several groups and migrating individual between them. By utilizing large number of processors, the optimization performance as well as the computation time were improved. Simulation results indicate that hybrid parallel EAs using the proposed structure have better performance in constrained numerical optimization problems than coarse-grained, or fine-grained parallel EAs, which are dedicated parallelization methods in previous work