Citation

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) Distance evaluated simulated Kalman filter for combinatorial optimization problems. ARPN Journal of Engineering and Applied Sciences, 11 (7). pp. 4911-4916. ISSN 1819-6608

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Abstract

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, a new distance evaluated approach is proposed and combined with SKF. The performance of the proposed distance evaluated SKF (DESKF) 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 DESKF. 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 DESKF and BGSA.

Item Type:	Article
Uncontrolled Keywords:	Simulated kalman filter, distance evaluated, combinatorial, and traveling salesman problems
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	16 Nov 2017 18:05
Last Modified:	21 Dec 2022 06:34
URII:	http://shdl.mmu.edu.my/id/eprint/6481

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