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An improved search space resizing method for model identification by standard genetic algorithm

Rajarathinam, K, Gomm, JB, Yu, DL and Abdelhadi, AS (2017) An improved search space resizing method for model identification by standard genetic algorithm. Systems Science and Control Engineering, 5 (1). pp. 117-128. ISSN 2164-2583

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In this paper, a new improved search space boundary resizing method for an optimal model’s parameter identification for continuous real time transfer function by standard genetic algorithms (SGAs) is proposed and demonstrated. Premature convergence to local minima, as a result of search space boundary constraints, is a key consideration in the application of SGAs. The new method improves the convergence to global optima by resizing or extending the upper and lower search boundaries. The resizing of the search space boundaries involves two processes, first, an identification of initial value by approximating the dynamic response period and desired settling time. Second, a boundary resizing method derived from the initial search space value. These processes brought the elite groups within feasible boundary regions by consecutive execution and enhanced the SGAs in locating the optimal model’s parameters for the identified transfer function. This new method is applied and examined on two processes, a third-order transfer function model with and without random disturbance and raw data of excess oxygen. The simulation results assured the new improved search space resizing method’s efficiency and flexibility in assisting SGAs to locate optimal transfer function model parameters in their explorations.

Item Type: Article
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher: Taylor & Francis Open
Date Deposited: 17 Mar 2017 11:58
Last Modified: 04 Sep 2021 11:48
DOI or ID number: 10.1080/21642583.2017.1289130
URI: https://researchonline.ljmu.ac.uk/id/eprint/6031
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