Pareto repeated weighted boosting search for multiple-objective optimisation
Pareto repeated weighted boosting search for multiple-objective optimisation
A guided stochastic search algorithm, known as the repeated weighted boosting search (RWBS), offers an effective means for solving the difficult single-objective optimisation problems with non-smooth and/or multi-modal cost functions. Compared with other global optimisation solvers, such as the genetic algorithms (GAs) and adaptive simulated annealing, RWBS is easier to implement, has fewer algorithmic parameters to tune and has been shown to provide similar levels of performance on many benchmark problems. This contribution develops a novel Pareto RWBS (PRWBS) algorithm for multiple objective optimisation applications. The performance of the proposed PRWBS algorithm is compared with the well-known non-dominated sorting GA (NSGA-II) for multiple objective optimisation on a range of benchmark problems, and the results obtained demonstrate that the proposed PRWBS algorithm offers a competitive performance whilst retaining the benefits of the original RWBS algorithm.
6 pages
Page, Scott
90c0a2b6-ca34-4ec9-a843-d39e4bfab446
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris
c4fd3763-7b3f-4db1-9ca3-5501080f797a
White, Neil
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
2011
Page, Scott
90c0a2b6-ca34-4ec9-a843-d39e4bfab446
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris
c4fd3763-7b3f-4db1-9ca3-5501080f797a
White, Neil
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Page, Scott, Chen, Sheng, Harris, Chris and White, Neil
(2011)
Pareto repeated weighted boosting search for multiple-objective optimisation.
11th UK Workshop on Computational Intelligence, Manchester, United Kingdom.
07 - 09 Sep 2011.
.
Record type:
Conference or Workshop Item
(Other)
Abstract
A guided stochastic search algorithm, known as the repeated weighted boosting search (RWBS), offers an effective means for solving the difficult single-objective optimisation problems with non-smooth and/or multi-modal cost functions. Compared with other global optimisation solvers, such as the genetic algorithms (GAs) and adaptive simulated annealing, RWBS is easier to implement, has fewer algorithmic parameters to tune and has been shown to provide similar levels of performance on many benchmark problems. This contribution develops a novel Pareto RWBS (PRWBS) algorithm for multiple objective optimisation applications. The performance of the proposed PRWBS algorithm is compared with the well-known non-dominated sorting GA (NSGA-II) for multiple objective optimisation on a range of benchmark problems, and the results obtained demonstrate that the proposed PRWBS algorithm offers a competitive performance whilst retaining the benefits of the original RWBS algorithm.
Text
ukci2011prwbs.pdf
- Version of Record
Text
UKCI2011-PRWBS.pdf
- Version of Record
More information
Published date: 2011
Additional Information:
Event Dates: September 7-9, 2011
Venue - Dates:
11th UK Workshop on Computational Intelligence, Manchester, United Kingdom, 2011-09-07 - 2011-09-09
Organisations:
EEE, Southampton Wireless Group
Identifiers
Local EPrints ID: 272721
URI: http://eprints.soton.ac.uk/id/eprint/272721
PURE UUID: bd073de3-1ded-424b-98f5-b2927c062292
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Date deposited: 25 Aug 2011 12:29
Last modified: 15 Mar 2024 02:41
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Contributors
Author:
Scott Page
Author:
Sheng Chen
Author:
Chris Harris
Author:
Neil White
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