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

Abstract— In this paper, we study parallelization of multiobjective optimization algorithms on a set of hetergeneous resources based on the Master-Slave model. Master-Slave model is known to be the simplest parallelization paradigm where a master processor sends the 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 from 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 contaning heterogeneous resources and ...

• Type: Conference
• Year: 2008
• Where: CEC
• Authors: Sanaz Mostaghim, Jürgen Branke, Andrew Lewis, Hartmut Schmeck
• Keywords: Artificial Intelligence, CEC 2008, Computing Resources, Heterogeneous Resources, Master-Slave Model