Interactive multi-objective particle swarm optimisation using decision space interaction

Jan Hettenhausen; Andrew Lewis; Marcus Randall; Timoleon Kipouros

doi:10.1109/CEC.2013.6557988

Interactive multi-objective particle swarm optimisation using decision space interaction

Jan Hettenhausen, Andrew Lewis, Marcus Randall, Timoleon Kipouros

Bond Business School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

24 Citations (Scopus)

304 Downloads (Pure)

Abstract

The most common approach to decision making in multi-objective optimisation with metaheuristics is a posteriori preference articulation. Increased model complexity and a gradual increase of optimisation problems with three or more objectives have revived an interest in progressively interactive decision making, where a human decision maker interacts with the algorithm at regular intervals. This paper presents an interactive approach to multi-objective particle swarm optimisation (MOPSO) using a novel technique to preference articulation based on decision space interaction and visual preference articulation. The approach is tested on a 2D aerofoil design case study and comparisons are drawn to non-interactive MOPSO.

Original language	English
Title of host publication	2013 IEEE Congress on Evolutionary Computation, CEC 2013
Place of Publication	online
Publisher	Wiley-IEEE Press
Pages	3411-3418
Number of pages	8
ISBN (Print)	9781479904549
DOIs	https://doi.org/10.1109/CEC.2013.6557988
Publication status	Published - 2013
Event	2013 IEEE Congress on Evolutionary Computation, CEC 2013 - Cancun, Cancun, Mexico Duration: 20 Jun 2013 → 23 Jun 2013

Conference

Conference	2013 IEEE Congress on Evolutionary Computation, CEC 2013
Country/Territory	Mexico
City	Cancun
Period	20/06/13 → 23/06/13

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10.1109/CEC.2013.6557988

Interactive multi-objective particle swarm optimisation using decision space interactionAccepted author manuscript, 1.9 MBLicence: Other

Cite this

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title = "Interactive multi-objective particle swarm optimisation using decision space interaction",

abstract = "The most common approach to decision making in multi-objective optimisation with metaheuristics is a posteriori preference articulation. Increased model complexity and a gradual increase of optimisation problems with three or more objectives have revived an interest in progressively interactive decision making, where a human decision maker interacts with the algorithm at regular intervals. This paper presents an interactive approach to multi-objective particle swarm optimisation (MOPSO) using a novel technique to preference articulation based on decision space interaction and visual preference articulation. The approach is tested on a 2D aerofoil design case study and comparisons are drawn to non-interactive MOPSO.",

author = "Jan Hettenhausen and Andrew Lewis and Marcus Randall and Timoleon Kipouros",

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doi = "10.1109/CEC.2013.6557988",

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pages = "3411--3418",

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note = "2013 IEEE Congress on Evolutionary Computation, CEC 2013 ; Conference date: 20-06-2013 Through 23-06-2013",

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Hettenhausen, J, Lewis, A, Randall, M & Kipouros, T 2013, Interactive multi-objective particle swarm optimisation using decision space interaction. in 2013 IEEE Congress on Evolutionary Computation, CEC 2013., 6557988, Wiley-IEEE Press, online, pp. 3411-3418, 2013 IEEE Congress on Evolutionary Computation, CEC 2013, Cancun, Mexico, 20/06/13. https://doi.org/10.1109/CEC.2013.6557988

Interactive multi-objective particle swarm optimisation using decision space interaction. / Hettenhausen, Jan; Lewis, Andrew; Randall, Marcus et al.
2013 IEEE Congress on Evolutionary Computation, CEC 2013. online: Wiley-IEEE Press, 2013. p. 3411-3418 6557988.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

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ER -

Interactive multi-objective particle swarm optimisation using decision space interaction

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