Asynchronous Master-Slave Parallelization of Differential Evolution for Multi-Objective Optimization
- 1 May 2013
- journal article
- Published by MIT Press in Evolutionary Computation
- Vol. 21 (2), 261-291
- https://doi.org/10.1162/evco_a_00076
Abstract
In this paper, we present AMS-DEMO, an asynchronous master-slave implementation of DEMO, an evolutionary algorithm for multi-objective optimization. AMS-DEMO was designed for solving time-intensive problems efficiently on both homogeneous and heterogeneous parallel computer architectures. The algorithm is used as a test case for the asynchronous master-slave parallelization of multi-objective optimization that has not yet been thoroughly investigated. Selection lag is identified as the key property of the parallelization method, which explains how its behavior depends on the type of computer architecture and the number of processors. It is arrived at analytically and from the empirical results. AMS-DEMO is tested on a benchmark problem and a time-intensive industrial optimization problem, on homogeneous and heterogeneous parallel setups, providing performance results for the algorithm and an insight into the parallelization method. A comparison is also performed between AMS-DEMO and generational master-slave DEMO to demonstrate how the asynchronous parallelization method enhances the algorithm and what benefits it brings compared to the synchronous method.Keywords
This publication has 33 references indexed in Scilit:
- Theory of the hypervolume indicatorPublished by Association for Computing Machinery (ACM) ,2009
- Parallel Evolutionary Computation Framework for Single- and Multiobjective OptimizationPublished by Springer Science and Business Media LLC ,2009
- Grid'5000: A Large Scale And Highly Reconfigurable Experimental Grid TestbedThe International Journal of High Performance Computing Applications, 2006
- Scalable Test Problems for Evolutionary Multiobjective OptimizationPublished by Springer Science and Business Media LLC ,2005
- Introduction to Evolutionary ComputingNatural Computing Series, 2003
- Parallel Evolutionary Optimization of Multibody Systems with Application to Railway DynamicsMultibody System Dynamics, 2003
- A fast and elitist multiobjective genetic algorithm: NSGA-IIIEEE Transactions on Evolutionary Computation, 2002
- Parallel evolutionary algorithms can achieve super-linear performanceInformation Processing Letters, 2002
- Improving flexibility and efficiency by adding parallelism to genetic algorithmsStatistics and Computing, 2002
- The Jackknife, the Bootstrap and Other Resampling PlansPublished by Society for Industrial & Applied Mathematics (SIAM) ,1982