One challenge for multi-objective genetic algorithm (MOGA) is the computational cost when MOGAs were used in the multidisciplinary optimization (MDO) problems. To improve the efficiency of MOGA, a new parallel algorithm was suggested. All the individuals were distributed among processors equally, and each processor got the extremum of Pareto solutions from all processors and constructed its own penalty function. Then each processor could divide its own Pareto solutions convergence area by the penalty function. To avoid the appearance of overlapping and omitting area and reduce the convergence time, some optimization techniques were suggested. So each processor could converge to its own special Pareto solutions segment. Because the individuals computed was divided into every processor equally, in each processor the computational cost was reduced. This with the small data changed in each processor guaranteed the efficiency. Through comparing with serial MOGA (NSGA2) and the other parallel MOGA (guided domination approach), the algorithm is proved being more effective and advanced.
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