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A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

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)

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, cybernetics and intelligent systems
T Technology > TA Engineering (General). Civil engineering (General) > TA 403 Materials Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 17 Sep 2015 16:48 UTC
Last Modified: 29 May 2019 16:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50506 (The current URI for this page, for reference purposes)
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