Sarro, F;
Petrozziello, A;
He, D-Q;
Yoo, S;
(2020)
A New Approach to Distribute MOEA Pareto Front Computation.
In:
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion: GECCO '20:.
(pp. pp. 315-316).
ACM: Cancún Mexico.
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Abstract
Multi-Objective Evolutionary Algorithms (MOEAs) offer compelling solutions to many real world problems, including software engineering ones. However, their efficiency decreases with the growing size of the problems at hand, hindering their applicability in practice. In this paper we propose a novel master-worker approach to distribute the computation of the Pareto Front (PF) for MOEAs (dubbed MOEA-DPF) and empirically evaluate it on a real-world software project management problem. With respect to previous work, our proposal can be used with any MOEA to tackle multiobjective problems regardless of their formulation/representation. Our results show that MOEA-DPF runs significantly faster (up to 3.1x speed-up using two workers) than its sequential counterpart while maintaining (and even improving) the quality of the PF. We conclude that MOEA-DPF provides an effective and simple solution to speed-up the execution of MOEAs by distributing the PF computation, making them effective for real-world problems.
| Type: | Proceedings paper |
|---|---|
| Title: | A New Approach to Distribute MOEA Pareto Front Computation |
| Event: | Genetic and Evolutionary Computation Conference |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3377929.3390024 |
| Publisher version: | https://doi.org/10.1145/3377929.3390024 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10096126 |
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