Estimation-based Metaheuristics: A New Branch of Computational Intelligence
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 fo...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/14583/1/P064%20pg469-476.pdf |
| _version_ | 1796991592405401600 |
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| author | Nor Hidayati, Abd Aziz Zuwairie, Ibrahim Saifudin, Razali Nor Azlina, Ab. Aziz |
| author_facet | Nor Hidayati, Abd Aziz Zuwairie, Ibrahim Saifudin, Razali Nor Azlina, Ab. Aziz |
| author_sort | Nor Hidayati, Abd Aziz |
| collection | UMP |
| description | 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 |
| first_indexed | 2024-03-06T12:07:45Z |
| format | Conference or Workshop Item |
| id | UMPir14583 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:07:45Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | UMPir145832018-02-08T02:47:52Z http://umpir.ump.edu.my/id/eprint/14583/ Estimation-based Metaheuristics: A New Branch of Computational Intelligence Nor Hidayati, Abd Aziz Zuwairie, Ibrahim Saifudin, Razali Nor Azlina, Ab. Aziz TK Electrical engineering. Electronics Nuclear engineering 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 2016 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/14583/1/P064%20pg469-476.pdf 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.. |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Nor Hidayati, Abd Aziz Zuwairie, Ibrahim Saifudin, Razali Nor Azlina, Ab. Aziz Estimation-based Metaheuristics: A New Branch of Computational Intelligence |
| title | Estimation-based Metaheuristics: A New Branch of Computational Intelligence
|
| title_full | Estimation-based Metaheuristics: A New Branch of Computational Intelligence
|
| title_fullStr | Estimation-based Metaheuristics: A New Branch of Computational Intelligence
|
| title_full_unstemmed | Estimation-based Metaheuristics: A New Branch of Computational Intelligence
|
| title_short | Estimation-based Metaheuristics: A New Branch of Computational Intelligence
|
| title_sort | estimation based metaheuristics a new branch of computational intelligence |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/14583/1/P064%20pg469-476.pdf |
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