NDLI: Enhancing MOPSO through the guidance of ANNs

Content Provider	IEEE Xplore Digital Library
Author	Rawlins, T. Lewis, A. Hettenhausen, J. Kipouros, T.
Copyright Year	2014
Description	Author affiliation: Griffith Univ., Griffith, NSW, Australia (Rawlins, T.; Lewis, A.; Hettenhausen, J.) \|\| Cambridge Univ., Cambridge, UK (Kipouros, T.)
Abstract	In existing work, Artificial Neural Networks (ANNs) are often used to model objective functions for Multi-Objective Particle Swarm Optimisation (MOPSO) or MOPSO is used to aid in ANN-training. We instead use an ANN to guide the optimisation algorithm by deciding if a trial solution is worthy of full evaluation. This should be particularly helpful for computationally expensive calculations. We also introduce a level of scepticism to the result produced by the ANN, to account both for inaccuracy in the ANN and the loss of performance in a MOPSO if the reinitialisation of particles is too extreme. As a case study we used a multi-objective optimisation problem that seeks to optimise the shape of an airfoil to minimise drag and maximise lift. We evaluated several different methods for training an ANN: pre-training vs live training, continuous vs single training, and varied initial training set size. For applying the ANN's output to MOPSO we looked at various levels of scepticism and verified ANN quality before applying it. Attainment surfaces were then used to compare the performance of guided and unguided MOPSOs. Our analysis showed the performance of guided MOPSO was significantly better than unguided MOPSO. We further analysed the results to derive guidance for selecting appropriate variations for specific problems.
Starting Page	4003
Ending Page	4010
File Size	690004
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889853
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Artificial neural networks Optimization Linear programming Particle swarm optimization Reliability
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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