Distributed computing of Pareto-optimal solutions using multi-objective evolutionary algorithms

In this paper, we suggest an approach for nding multiple Pareto-optimal solutions with a distributed computing system. When the number of objective functions are more, the resulting Paretooptimal set is large, thereby requiring a single processor multi-objective EA (MOEA) approach to use a large population size to be run for a large number of generations. However, the task of nding the complete Pareto-optimal front can be distributed among a number of processors, each pre-destined to nd a particular region of the Pareto-optimal set. Based on the guided domination approach, here we propose a modi ed domination criterion for this task. The proof-of-principle results obtained with a parallel version of NSGA-II shows the ecacy of the proposed approach.