Paper Info
Title
Asynchronous Master-Slave Parallelization of Differential Evolution for Multi-Objective Optimization
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.
Year
DOI
Venue
2013
10.1162/EVCO_a_00076
Evolutionary Computation
Keywords
Field
DocType
Multi-objective optimization,differential evolution,distributed computing,evolutionary algorithms,parallelization,selection lag,speedup
Asynchronous communication,Mathematical optimization,Evolutionary algorithm,Computer science,Parallel computing,Differential evolution,Multi-objective optimization,Master/slave,Optimization problem,Speedup,Automatic parallelization
Journal
Volume
Issue
ISSN
21
2
1063-6560
Citations 
PageRank 
References 
19
0.93
31
Authors
4
Name
Order
Citations
PageRank
Matjaž Depolli1508.70
Roman Trobec218038.90
Filipič, B3190.93
Bogdan Filipic436126.93