Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems
This paper introduces single-solution Simulated Kalman Filter (ssSKF), a new single-agent optimisa- tion algorithm inspired by Kalman Filter, for solving real-valued numerical optimisation problems. In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Indian Academy of Sciences
2016
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/19727/ http://umpir.ump.edu.my/id/eprint/19727/1/Hidayati%20et%20al.%20-%20Single-solution%20simulated%20Kalman%20filter%20algorithm%20for%20global%20optimisation%20problems%20-%20Unknown%20-%20Unknown.pdf |
| Summary: | This paper introduces single-solution Simulated Kalman Filter (ssSKF), a new single-agent optimisa- tion algorithm inspired by Kalman Filter, for solving real-valued numerical optimisation problems. In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. In the proposed ssSKF algorithm, the initialisation parameters are not constants, but, are produced by random numbers taken from a normal dis- tribution in the range of [0, 1], thus excluding them from tuning requirement. In order to balance between the exploration and exploitation in ssSKF, the proposed algorithm uses an adaptive neighbourhood mechanism during its prediction step. The proposed ssSKF algorithm is tested using the 30 benchmark functions of CEC 2014, and its performance is compared to the original SKF algorithm, Black Hole (BH) algorithm, Particle Swarm Optimisation (PSO) algorithm, Grey Wolf Optimiser (GWO) algorithm, and Genetic Algorithm (GA). The results show that the proposed ssSKF algorithm is a promising approach and able to outperform GWO and GA algorithms, significantly. |
|---|