Universiti Teknologi Malaysia Institutional Repository

Online logistics booking system utilizing artificial intelligence

Ghazal, Hisham Mohamad Malik and Sahlan, Shafishuhaza and Selamat, Hazlina and Ismail, Fatimah Sham (2022) Online logistics booking system utilizing artificial intelligence. In: Computational Intelligence in Machine Learning Select Proceedings of ICCIML 2021. Lecture Notes in Electrical Engineering, 834 (NA). Springer Science and Business Media Deutschland GmbH, Singapore, pp. 347-356. ISBN 978-981168483-8

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Official URL: http://dx.doi.org/10.1007/978-981-16-8484-5_33

Abstract

In this paper, the application of genetic algorithm (GA) in linear and non-linear dynamic modeling, as well as the development of an alternative GA-based model structure selection algorithm is presented. As a benchmark for the proposed algorithm, orthogonal least square (OLS), a gradient descent method, is utilized. To reduce problems of premature convergence in simple GA (SGA), a model structure selection based on modified genetic algorithm (MGA) is proposed. The effect of different combinations of MGA operators on the performance of the developed model was studied, and the effectiveness and shortcomings of MGA were highlighted. Results were compared between SGA, MGA, and OLS. From the results obtained, it was observed that with similar number of dynamic terms, MGA performs better than SGA, in terms of exploring potential solution, meanwhile outperformed the OLS algorithm in terms of selected number of terms and predictive accuracy. In addition, for fine-tuning, the use of local search of memetic algorithm (MA) with MGA is also proposed and investigated. From the results obtained, it can be observed that MA can produce an adequate which satisfy the model validation tests with significant advantages over OLS, SGA, and MGA methods.

Item Type:Book Section
Uncontrolled Keywords:artificial intelligence, genetic algorithm, modified genetic algorithm, orthogonal least square, simple genetic algorithm
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Faculty of Engineering - School of Electrical
ID Code:100673
Deposited By: Yanti Mohd Shah
Deposited On:30 Apr 2023 08:32
Last Modified:30 Apr 2023 08:32

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