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New hybrid genetic operators for real coded genetic algorithm to compute optimal control of a class of hybrid systems

Arumugam, M.S., Rao, M.V.C., Palaniappan, Ramaswamy (2005) New hybrid genetic operators for real coded genetic algorithm to compute optimal control of a class of hybrid systems. Applied Soft Computing, 6 (1). pp. 38-52. ISSN 1568-4946. (doi:10.1016/j.asoc.2004.11.001) (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)

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. (Contact us about this Publication)
Official URL
https://doi.org/10.1016/j.asoc.2004.11.001

Abstract

This paper introduces new hybrid cross-over methods and new hybrid selection methods for real coded genetic algorithm (RCGA), to solve the optimal control problem of a class of hybrid system, which is motivated by the structure of manufacturing environments that integrate process and optimal control. In this framework, the discrete entities have a state characterized by a temporal component whose evolution is described by event-driven dynamics and a physical component whose evolution is described by continuous time-driven systems. The proposed RCGA with hybrid genetic operators can outperform the conventional RCGA and the existing Forward Algorithms for this class of systems. The hybrid genetic operators improve both the quality of the solution and the actual optimum value of the objective function. A typical numerical example of the optimal control problem with the number of jobs varying from 5 to 25 is included to illustrate the efficacy of the proposed algorithm. Several statistical analyses are done to compare the betterment of the proposed algorithm over the conventional RCGA and Forward Algorithm. Hypothesis t-test and Analysis of Variance (ANOVA) test are also carried out to validate the effectiveness of the proposed algorithm. © 2004 Elsevier B.V. All rights reserved.

Item Type: Article
DOI/Identification number: 10.1016/j.asoc.2004.11.001
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Appl. Soft Comput. J. [Field not mapped to EPrints] AD - Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh, 75450 Malacca, Malaysia [Field not mapped to EPrints] AD - Biomedical Engineering Research Centre, Nanyang Technological University, 637553 Singapore, Singapore [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints]
Uncontrolled keywords: Genetic algorithm (GA), Hybrid genetic operators, Hybrid systems, Optimal control, Real coded genetic algorithm (RCGA), Genetic algorithms, Hybrid computers, Numerical analysis, Optimal control systems, Statistical methods, Time domain analysis, Genetic algorithm (GA), Hybrid genetic operators, Hybrid systems, Optimal control, Real coded genetic algorithm (RCGA), Mathematical operators
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 18:27 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70748 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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