Parallelizing multi-objective evolutionary algorithms: cone separation

Branke, J. ; Schmeck, H. ; Deb, K. ; Reddy, S. M. (2004) Parallelizing multi-objective evolutionary algorithms: cone separation Proceedings of the Congress on Evolutionary Computation (CEC-2004), 2 . pp. 1952-1957.

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Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...

Related URL: http://dx.doi.org/10.1109/CEC.2004.1331135

Abstract

Evolutionary multi-objective optimization (EMO) may be computationally quite demanding, because instead of searching for a single optimum, one generally wishes to find the whole front of Pareto-optimal solutions. For that reason, parallelizing EMO is an important issue. Since we are looking for a number of Pareto-optimal solutions with different tradeoffs between the objectives, it seems natural to assign different parts of the search space to different processors. We propose the idea of cone separation which is used to divide up the search space by adding explicit constraints for each process. We show that the approach is more efficient than simple parallelization schemes, and that it also works on problems with a non-convex Pareto-optimal front.

Item Type:Article
Source:Copyright of this article belongs to Proceedings of the Congress on Evolutionary Computation (CEC-2004).
ID Code:81667
Deposited On:07 Feb 2012 05:28
Last Modified:18 May 2016 23:08

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