A parallel framework for multi-objective evolutionary optimization
Dipankar Dasgupta, David Becerra, Alex Banceanu, Fernando Nino, James Simien · 2010
This work focuses on the development of a parallel framework method to improve the effectiveness and the efficiency of the obtained solutions by Multi-objective Evolutionary Algorithms. Specifically, a parallel architecture based on JavaSpaces technology and an island paradigm model is proposed and tested on two important and complex computational problems: The Protein Structure Prediction and the Task based Navy's Sailor Assignment problems. An experimental framework is developed in order to test the proposed parallel framework. Particularly, the framework is tested using real-world data belonging to the TSAP and PSP problem. Furthermore, new insights are obtained about modeling these problems as parallel Multi-objective Evolutionary Algorithms.