An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design
In the past few decades, metaheuristic algorithms (MA) have been developed tremendously and have been successfully applied in many fields. In recent years, a large number of new MA have been proposed. Slime mould algorithm (SMA) is a novel swarm-based intelligence optimization algorithm. SMA solves...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/5333278 |
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| author | Xiaodan Liang Dong Wu Yang Liu Maowei He Liling Sun |
| author_facet | Xiaodan Liang Dong Wu Yang Liu Maowei He Liling Sun |
| author_sort | Xiaodan Liang |
| collection | DOAJ |
| description | In the past few decades, metaheuristic algorithms (MA) have been developed tremendously and have been successfully applied in many fields. In recent years, a large number of new MA have been proposed. Slime mould algorithm (SMA) is a novel swarm-based intelligence optimization algorithm. SMA solves the optimization problem by imitating the foraging and movement behavior of slime mould. It can effectively obtain a promising global optimal solution. However, it still suffers some shortcomings such as the unstable convergence speed, the imprecise search accuracy, and incapability of identifying a local optimal solution when faced with complicated optimization problems. With the purpose of overcoming the shortcomings of SMA, this paper proposed a multistrategy enhanced version of SMA called ESMA. The three enhanced strategies are chaotic initialization strategy (CIS), orthogonal learning strategy (OLS), and boundary reset strategy (BRS). The CIS is used to generate an initial population with diversity in the early stage of ESMA, which can increase the convergence speed of the algorithm and the quality of the final solution. Then, the OLS is used to discover the useful information of the best solutions and offer a potential search direction, which enhances the local search ability and raises the convergence rate. Finally, the BRS is used to correct individual positions, which ensures the population diversity and enhances the overall search capabilities of ESMA. The performance of ESMA was validated on the 30 IEEE CEC2014 functions and three IIR model identification problems, compared with other nine well-regarded and state-of-the-art algorithms. Simulation results and analysis prove that the ESMA has a superior performance. The three strategies involved in ESMA have significantly improved the performance of the basic SMA. |
| format | Article |
| id | doaj-art-1001035eb31d4585a6285c43e62cabb8 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-1001035eb31d4585a6285c43e62cabb82025-02-03T01:26:55ZengWileyDiscrete Dynamics in Nature and Society1607-887X2021-01-01202110.1155/2021/5333278An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter DesignXiaodan Liang0Dong Wu1Yang Liu2Maowei He3Liling Sun4School of Computer Science and TechnologySchool of Computer Science and TechnologyLiaoning InspectionSchool of Computer Science and TechnologySchool of Computer Science and TechnologyIn the past few decades, metaheuristic algorithms (MA) have been developed tremendously and have been successfully applied in many fields. In recent years, a large number of new MA have been proposed. Slime mould algorithm (SMA) is a novel swarm-based intelligence optimization algorithm. SMA solves the optimization problem by imitating the foraging and movement behavior of slime mould. It can effectively obtain a promising global optimal solution. However, it still suffers some shortcomings such as the unstable convergence speed, the imprecise search accuracy, and incapability of identifying a local optimal solution when faced with complicated optimization problems. With the purpose of overcoming the shortcomings of SMA, this paper proposed a multistrategy enhanced version of SMA called ESMA. The three enhanced strategies are chaotic initialization strategy (CIS), orthogonal learning strategy (OLS), and boundary reset strategy (BRS). The CIS is used to generate an initial population with diversity in the early stage of ESMA, which can increase the convergence speed of the algorithm and the quality of the final solution. Then, the OLS is used to discover the useful information of the best solutions and offer a potential search direction, which enhances the local search ability and raises the convergence rate. Finally, the BRS is used to correct individual positions, which ensures the population diversity and enhances the overall search capabilities of ESMA. The performance of ESMA was validated on the 30 IEEE CEC2014 functions and three IIR model identification problems, compared with other nine well-regarded and state-of-the-art algorithms. Simulation results and analysis prove that the ESMA has a superior performance. The three strategies involved in ESMA have significantly improved the performance of the basic SMA.http://dx.doi.org/10.1155/2021/5333278 |
| spellingShingle | Xiaodan Liang Dong Wu Yang Liu Maowei He Liling Sun An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design Discrete Dynamics in Nature and Society |
| title | An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design |
| title_full | An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design |
| title_fullStr | An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design |
| title_full_unstemmed | An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design |
| title_short | An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design |
| title_sort | enhanced slime mould algorithm and its application for digital iir filter design |
| url | http://dx.doi.org/10.1155/2021/5333278 |
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