Zhang, Qian, Mahfouf, Mahdi (2010) A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels. Engineering Applications of Artificial Intelligence, 23 (5). pp. 660-675. ISSN 0952-1976. (doi:10.1016/j.engappai.2010.01.017) (KAR id:50506)
PDF
Pre-print
Language: English |
|
Download this file (PDF/2MB) |
|
Request a format suitable for use with assistive technology e.g. a screenreader | |
Official URL: http://doi.org/10.1016/j.engappai.2010.01.017 |
Abstract
In this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ‘real-life’ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals.
Item Type: | Article |
---|---|
DOI/Identification number: | 10.1016/j.engappai.2010.01.017 |
Uncontrolled keywords: | Nature-Inspired Algorithm, Search Strategy, Reduced Space Searching, Multi-Objective Optimisation, Evolutionary Algorithms, Optimal Design, Alloy Steel, Mechanical Property, Tensile Strength |
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: | 17 Sep 2015 16:48 UTC |
Last Modified: | 16 Nov 2021 10:21 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/50506 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):