Mahfouf, Mahdi, Chen, Min-You, Zhang, Qian, Linkens, Derek A. (2006) Adaptive weighted particle Swarm multiobjective optimisation and societal reasoning for the design o. In: The 1st IFAC Workshop on Applications of Large Scale Industrial Systems, 30-31 August 2006, Helsinki, Finland. (doi:10.3182/20060830-2-SF-4903.00023) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:50556)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
Official URL: http://doi.org/10.3182/20060830-2-SF-4903.00023 |
Abstract
A recent shift in optimisation research suggests two trends: one is associated with a move to multi-objective optimisation where challenges of non-unique solutions and the concept of feasibility lie, while the other relates to the development of 'intelligent' algorithms which draw their philosophies from nature and biological intricacies. In this spirit, we propose two new multi-objective optimisation algorithms, a modified PSO algorithm and a societal reasoning method named Reduced Space Searching Algorithm. This paper shows how these algorithms compare to other algorithms and how they can be used to solve a real-world problem, the multi-objective optimal design of alloy steels.
Item Type: | Conference or workshop item (Paper) |
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DOI/Identification number: | 10.3182/20060830-2-SF-4903.00023 |
Subjects: |
Q Science > Q Science (General) > Q335 Artificial intelligence T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | Qian Zhang |
Date Deposited: | 18 Sep 2015 16:43 UTC |
Last Modified: | 05 Nov 2024 10:36 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/50556 (The current URI for this page, for reference purposes) |
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