Abstract
This article provides an effective way to alleviate the long running defect of multi-objective intelligent optimization algorithms and enhance its ability to solve the robust optimization (RO) design. It describes the implementation of parallel computing for intelligent optimization algorithms based on the MATLAB Parallel Computing Toolbox (PCT) and Distributed Computing Server (DCS). In order to test the effectiveness of the proposed method, the parallel multi-objective bacterial colony chemo taxis (PMOBCC) algorithm and parallel dichotomy (PD) are applied to run a class of bi-level inverse multi-objective robust optimization (BI-MORO) design. A set of experiments are tested by using different number of MATLAB workers. The results illustrated that it is convenient and effective to use MATLAB for parallelizing the intelligent optimization algorithms.
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