A Reliable Parallel Interval Global Optimization Algorithm Based on Mind Evolutionary Computation

Abstract

In this paper, we investigate the parallel reliable computational model and propose a parallel interval evolutionary algorithm that integrates interval arithmetic and Mind Evolutionary Computation method. The major aim is to explorer the new parallel interval decomposition scheme can solve computation intensive problem and can determine the all optimal solution reliably. The proposed algorithm is experimentally testified on the ZiQiang 3000 cluster of Shanghai High Education Grid-e-Grid Computational Application Platform with a test suit containing 6 complex multi-modal function optimization benchmarks.