Angle modulated simulated Kalman filter algorithm for combinatorial optimization problems


Md Yusof, Zulkifli and Ibrahim, Zuwairie and Ibrahim, Ismail and Mohd Azmi, Kamil Zakwan and Ab Aziz, Nor Azlina and Abdul Aziz, Nor Hidayati and Mohamad, Mohd Saberi (2016) Angle modulated simulated Kalman filter algorithm for combinatorial optimization problems. ARPN Journal of Engineering and Applied Sciences, 11 (7). pp. 4854-4859. ISSN 1819-6608

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Inspired by the estimation capability of Kalman filter, we have recently introduced a novel estimation-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agent estimates the global minimum/maximum. Measurement, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process. However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve discrete optimization problems, the SKF algorithm is combined with an angle modulated approach. The performance of the proposed angle modulated SKF (AMSKF) is compared against two other discrete population-based optimization algorithms, namely, binary particle swarm optimization (BPSO) and binary gravitational search algorithm (BGSA). A set of traveling salesman problems are used to evaluate the performance of the proposed AMSKF. Based on the analysis of experimental results, we found that the proposed AMSKF is as competitive as BGSA but the BPSO is superior to the both AMSKF and BGSA.

Item Type: Article
Uncontrolled Keywords: Simulated kalman filter, angle modulated, combinatorial, traveling salesman problems.
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 16 Nov 2017 18:10
Last Modified: 21 Dec 2022 06:33


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