Local Optimum Distance Evaluated Simulated Kalman Filter For Combinatorial Optimization Problems

Local Optimum Distance Evaluated Simulated Kalman Filter For Combinatorial Optimization Problems

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 agen...

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Main Authors: Zulkifli, Md. Yusof, Ismail, Ibrahim, Zuwairie, Ibrahim, Khairul Hamimah, Abas, Nor Azlina, Ab. Aziz, Nor Hidayati, Abd Aziz, Mohd Saberi, Mohamad
Format: Conference or Workshop Item
Language:English
Published: Universiti Malaysia Pahang 2016
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/15720/
http://umpir.ump.edu.my/id/eprint/15720/
http://umpir.ump.edu.my/id/eprint/15720/1/P119%20pg892-901.pdf
id ump-15720
recordtype eprints
spelling ump-157202018-02-08T03:02:37Z http://umpir.ump.edu.my/id/eprint/15720/ Local Optimum Distance Evaluated Simulated Kalman Filter For Combinatorial Optimization Problems Zulkifli, Md. Yusof Ismail, Ibrahim Zuwairie, Ibrahim Khairul Hamimah, Abas Nor Azlina, Ab. Aziz Nor Hidayati, Abd Aziz Mohd Saberi, Mohamad TK Electrical engineering. Electronics Nuclear engineering 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 combinatorial optimization problems, an extended version of SKF algorithm, which is termed as Local Optimum Distance Evaluated Simulated Kalman Filter (LODESKF), is proposed. Similar to existing approach, a mapping function is used to enable the SKF algorithm to operate in binary search space. A set of traveling salesman problems are used to evaluate the performance of the proposed LODESKF against DESKF Universiti Malaysia Pahang 2016 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/15720/1/P119%20pg892-901.pdf Zulkifli, Md. Yusof and Ismail, Ibrahim and Zuwairie, Ibrahim and Khairul Hamimah, Abas and Nor Azlina, Ab. Aziz and Nor Hidayati, Abd Aziz and Mohd Saberi, Mohamad (2016) Local Optimum Distance Evaluated Simulated Kalman Filter For Combinatorial Optimization Problems. In: Proceedings of The National Conference for Postgraduate Research (NCON-PGR 2016), 24-25 September 2016 , Universiti Malaysia Pahang (UMP), Pekan, Pahang. pp. 892-901.. http://umpir.ump.edu.my/15720/1/P119%20pg892-901.pdf
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Zulkifli, Md. Yusof
Ismail, Ibrahim
Zuwairie, Ibrahim
Khairul Hamimah, Abas
Nor Azlina, Ab. Aziz
Nor Hidayati, Abd Aziz
Mohd Saberi, Mohamad
Local Optimum Distance Evaluated Simulated Kalman Filter For Combinatorial Optimization Problems
description 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 combinatorial optimization problems, an extended version of SKF algorithm, which is termed as Local Optimum Distance Evaluated Simulated Kalman Filter (LODESKF), is proposed. Similar to existing approach, a mapping function is used to enable the SKF algorithm to operate in binary search space. A set of traveling salesman problems are used to evaluate the performance of the proposed LODESKF against DESKF
format Conference or Workshop Item
author Zulkifli, Md. Yusof
Ismail, Ibrahim
Zuwairie, Ibrahim
Khairul Hamimah, Abas
Nor Azlina, Ab. Aziz
Nor Hidayati, Abd Aziz
Mohd Saberi, Mohamad
author_facet Zulkifli, Md. Yusof
Ismail, Ibrahim
Zuwairie, Ibrahim
Khairul Hamimah, Abas
Nor Azlina, Ab. Aziz
Nor Hidayati, Abd Aziz
Mohd Saberi, Mohamad
author_sort Zulkifli, Md. Yusof
title Local Optimum Distance Evaluated Simulated Kalman Filter For Combinatorial Optimization Problems
title_short Local Optimum Distance Evaluated Simulated Kalman Filter For Combinatorial Optimization Problems
title_full Local Optimum Distance Evaluated Simulated Kalman Filter For Combinatorial Optimization Problems
title_fullStr Local Optimum Distance Evaluated Simulated Kalman Filter For Combinatorial Optimization Problems
title_full_unstemmed Local Optimum Distance Evaluated Simulated Kalman Filter For Combinatorial Optimization Problems
title_sort local optimum distance evaluated simulated kalman filter for combinatorial optimization problems
publisher Universiti Malaysia Pahang
publishDate 2016
url http://umpir.ump.edu.my/id/eprint/15720/
http://umpir.ump.edu.my/id/eprint/15720/
http://umpir.ump.edu.my/id/eprint/15720/1/P119%20pg892-901.pdf
first_indexed 2023-09-18T22:20:41Z
last_indexed 2023-09-18T22:20:41Z
_version_ 1777415636246331392

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