A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and Swap Operator

Suhazri Amrin, Rahmad and Zuwairie, Ibrahim and Zulkifli, Md. Yusof (2021) A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and Swap Operator. In: IEEE International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE 2021) , 20-21 October 2021 , Banda Aceh, Indonesia. pp. 181-185.. ISBN 978-1-6654-2509-4

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Abstract

The simulated Kalman filter (SKF) is an algorithm for population-based optimization based on the Kalman filter framework. Each agent in SKF is treated as a Kalman filter. The SKF utilizes a Kalman filter process that includes prediction, measurement, and estimation to determine the global optimum. However, the SKF can only operate in the numerical search space. Numerous approaches and modifications have been used in the literature to enable numerical meta-heuristic algorithms to operate in a discrete search space. This paper presents modifications to measurement and estimation in the SKF by utilizing mutation and Hamming distance technique to accommodate the discrete search space. The modified algorithm is called Discrete Simulated Kalman Filter Optimizer (DSKFO). Additionally, the DSKFO algorithm incorporates the swap operator as an extension to improve the solution in solving the travelling salesman problem (TSP). The DSKFO algorithm was compared against four other combinatorial SKF algorithms and outperformed them all.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Combinatorial, Simulated Kalman Filter, Swap Operator, Travelling Salesman Problem
Subjects: T Technology > TS Manufactures
Faculty/Division: Institute of Postgraduate Studies
College of Engineering
Faculty of Mechanical & Manufacturing Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 07 Feb 2022 02:33
Last Modified: 07 Feb 2022 02:33
URI: http://umpir.ump.edu.my/id/eprint/33307
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