Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems

Zulkifli, Md. Yusof and Zuwairie, Ibrahim and Ismail, Ibrahim and Kamil Zakwan, Mohd Azmi and Nor Azlina, Ab. Aziz and Nor Hidayati, Abd. Aziz and Mohd Saberi, Mohamad (2016) Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems. ARPN Journal of Engineering and Applied Sciences, 11 (7). 4854 -4859. ISSN 1819-6608. (Published)

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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 than the both AMSKF and BGSA

Item Type: Article
Uncontrolled Keywords: Simulated Kalman Filter, Angle Modulated, Combinatorial, Traveling Salesman Problems
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 30 Nov 2015 06:29
Last Modified: 30 Oct 2017 02:30
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