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..
|
PDF
P119 pg892-901.pdf Download (529kB) | Preview |
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 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
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Uncontrolled Keywords: | simulated kalman filter, traveling salesman problem, combinatorial optimization |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering |
| Depositing User: | Rosfadilla Mohamad Zainun |
| Date Deposited: | 09 Dec 2016 03:20 |
| Last Modified: | 08 Feb 2018 03:02 |
| URI: | http://umpir.ump.edu.my/id/eprint/15720 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |