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...

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Bibliographic Details
Published in:Procedia Computer Science
Main Authors: Curtis, M, Lewis, A
Format: Conference Object
Language:English
Published: Elsevier 2015
Subjects:
Optimisation
Information and computing sciences
Numerical computation and mathematical software
Griffith
Iceland
Online Access:http://hdl.handle.net/10072/104582
https://doi.org/10.1016/j.procs.2015.05.435
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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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