Reduction of Computational Load for MOPSO
The run time for many optimisation algorithms, particularly those that explicitly consider multiple objectives, can be impractically large when applied to real world problems. This paper reports an investigation into the behaviour of Multi-Objective Particle Swarm Optimisation (MOPSO), which seeks t...
Published in: | Procedia Computer Science |
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Main Authors: | , |
Format: | Conference Object |
Language: | English |
Published: |
Elsevier
2015
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Subjects: | |
Online Access: | http://hdl.handle.net/10072/104582 https://doi.org/10.1016/j.procs.2015.05.435 |
Summary: | The run time for many optimisation algorithms, particularly those that explicitly consider multiple objectives, can be impractically large when applied to real world problems. This paper reports an investigation into the behaviour of Multi-Objective Particle Swarm Optimisation (MOPSO), which seeks to reduce the number of objective function evaluations needed, with-out degrading solution quality. By restricting archive size and strategically reducing the trial solution population size, it has been found the number of function evaluations can be reduced by 66.7% without significant reduction in solution quality. In fact, careful manipulation of algorithm operating parameters can even significantly improve solution quality. Griffith Sciences, School of Information and Communication Technology Full Text |
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