Simulated kalman filter: a novel estimation-based metaheuristic optimization algorithm
In this paper, a novel population - based metaheuristic optimization algorithm , which is named as Simulated Kalman Filter (SKF) , is introduced for global optimization problem . This new algorithm is inspired by the estimation capability of the well - known Kalman Filter. In principl...
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| Main Authors: | , , , , |
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| 格式: | Article |
| 出版: |
American Scientific Publishers
2016
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| 主题: | |
| 在线阅读: | http://eprints.utm.my/68354/ http://eprints.utm.my/68354/ |
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| 总结: | In this paper, a novel population - based metaheuristic optimization algorithm , which is named as Simulated Kalman Filter (SKF) , is introduced for global optimization problem . This new algorithm is inspired by the estimation capability of the well - known 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 framewo rk, which includes a simulated measurement process and a best - so - far solution as a reference. To evaluate the performance of the SKF algorithm, it is applied to 30 benchmark functions of CEC 2014 for real - parameter single - objective optimization problems. S tatistical analysis is then carried out to rank SKF results to those obtained by other metaheuristic algo rithms. The experimental results show that the proposed SKF algorithm is a promising approach and able to outperform some well - known metaheuristic algo rithms , such as Genetic Algorithm, Particle Swarm Optimization, Black Hole Algor ithm, and Grey Wolf Optimizer. |
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