Nor Hidayati, Abd Aziz and Zuwairie, Ibrahim and Saifudin, Razali and Nor Azlina, Ab. Aziz (2016) Estimation-based Metaheuristics: A New Branch of Computational Intelligence. In: Proceedings of The National Conference for Postgraduate Research (NCON-PGR 2016) , 24-25 September 2016 , Universiti Malaysia Pahang (UMP), Pekan, Pahang. pp. 469-476.. (Published)
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Abstract
In this paper, a new branch of computational intelligence named estimation-based metaheuristic is introduced. Metaheuristic algorithms can be classified based on their source of inspiration. Besides biology, physics and chemistry, state estimation algorithm also has become a source of inspiration for developing metaheuristic algorithms. Inspired by the estimation capability of Kalman Filter, Simulated Kalman Filter, SKF, uses a population of agents to make estimations of the optimum. Each agent in SKF acts as a Kalman Filter. By adapting the standard Kalman Filter framework, each individual agent finds an optimization solution by using a simulated measurement process that is guided by a best-so-far solution as a reference. Heuristic Kalman Algorithm (HKA) also is inspired by the Kalman Filter framework. HKA however, explicitly consider the optimization problem as a measurement process in generating the estimate of the optimum. In evaluating the performance of the estimation-based algorithms, it is implemented to 30 benchmark functions of the CEC 2014 benchmark suite. Statistical analysis is then carried out to rank the estimation-based algorithms’ results to those obtained by other metaheuristic algorithms. The experimental results show that the estimation-based metaheuristic is a promising approach to solving global optimization problem and demonstrates a competitive performance to some well-known metaheuristic algorithms
| Item Type: | Conference or Workshop Item (Lecture) |
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| Uncontrolled Keywords: | metaheuristic optimization; estimation-based; Kalman Filter; SKF; HKA |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering |
| Depositing User: | Noorul Farina Arifin |
| Date Deposited: | 30 Sep 2016 08:19 |
| Last Modified: | 08 Feb 2018 02:47 |
| URI: | http://umpir.ump.edu.my/id/eprint/14583 |
| Download Statistic: | View Download Statistics |
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