Parallel multi-objective optimization using Master-Slave model on heterogeneous resources

In this paper, we study parallelization of multi-objective optimization algorithms on a set of heterogeneous resources based on the Master-Slave model. The Master-Slave model is known to be the simplest parallelization paradigm, where a master processor sends function evaluations to several slave processors. The critical issue when using the standard methods on heterogeneous resources is that in every iteration of the optimization, the master processor has to wait for all of the computing resources (including the slow ones) to deliver the evaluations. In this paper, we study a new algorithm where all of the available computing resources are efficiently utilized to perform the multi-objective optimization task independent of the speed (fast or slow) of the computing processors. For this we propose a hybrid method using Multi-objective Particle Swarm optimization and Binary search methods. The new algorithm has been tested on a scenario containing heterogeneous resources and the results show that not only does the new algorithm perform well for parallel resources, but also when compared to a normal serial run on one computer.

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