Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm
In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. This new algorithm is inspired by the estimation capability of the Kalman Filter. In principle, state estimation problem is regarded as an optimization problem, and each age...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Advanced Science Letters
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/12020/ http://umpir.ump.edu.my/id/eprint/12020/1/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm.pdf http://umpir.ump.edu.my/id/eprint/12020/7/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm-%20abstract.pdf |
| Summary: | In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. This new algorithm is inspired by the estimation capability of the Kalman Filter. In principle, state estimation problem is regarded as an optimization problem, and each agent in SKF acts as a Kalman Filter. An agent in the population finds solution to optimization problem using a standard Kalman Filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. To evaluate the performance of the Simulated Kalman Filter algorithm, it is applied to 30 benchmark functions of CEC 2014 for real-parameter single objective optimization problems. Statistical analysis is then carried out to rank SKF results to those obtained by other metaheuristic algorithms. The experimental results show that the proposed SKF algorithm is a promising approach, and has a comparable performance to some well-known metaheuristic algorithms. |
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