Performance Evaluation of Hybrid SKF Algorithms: Hybrid SKF-PSO and Hybrid SKF-GSA
This paper presents a performance evaluation of hybrid Simulated Kalman Filter Gravitational Algorithm (SKF-GSA), and hybrid Simulated Kalman Filter Particle Swarm Optimization (SKF-PSO), for continuous numerical optimization problems. Simulated Kalman filter (SKF) was inspired by the estimation c...
| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Universiti Malaysia Pahang
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/15724/ http://umpir.ump.edu.my/id/eprint/15724/ http://umpir.ump.edu.my/id/eprint/15724/1/P116%20pg865-874.pdf |
| Summary: | This paper presents a performance evaluation of hybrid Simulated Kalman Filter Gravitational Algorithm (SKF-GSA),
and hybrid Simulated Kalman Filter Particle Swarm Optimization (SKF-PSO), for continuous numerical optimization problems.
Simulated Kalman filter (SKF) was inspired by the estimation capability of Kalman filter. Every agent in SKF is regarded as a Kalman filter. The performance of the hybrid algorithms (SKF-GSA and SKF-PSO) is compared using CEC2014 benchmark dataset for continuous numerical optimization problems. Based on the analysis of experimental results, we found that the SKF-PSO performs the best among all. |
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