Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy
This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhanc...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/7/973 |
| _version_ | 1797433615639904256 |
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| author | Qingyu Xia Yuanming Ding Ran Zhang Huiting Zhang Sen Li Xingda Li |
| author_facet | Qingyu Xia Yuanming Ding Ran Zhang Huiting Zhang Sen Li Xingda Li |
| author_sort | Qingyu Xia |
| collection | DOAJ |
| description | This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhance the population’s diversity and ergodicity. Then, inspired by the sigmoid function, a new parameter is designed to strengthen the ability of the algorithm to coordinate early exploration and late development. Finally, the particle swarm optimization learning strategy is introduced into the seagull position updating method to improve the ability of the algorithm to jump out of local optimization. Through the simulation comparison with other algorithms on 12 benchmark test functions from different angles, the experimental results show that SPSOA is superior to other algorithms in stability, convergence accuracy, and speed. In engineering applications, SPSOA is applied to blind source separation of mixed images. The experimental results show that SPSOA can successfully realize the blind source separation of noisy mixed images and achieve higher separation performance than the compared algorithms. |
| first_indexed | 2024-03-09T10:19:31Z |
| format | Article |
| id | doaj.art-ad22046bc57947dab2ac854ffb2da666 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T10:19:31Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-ad22046bc57947dab2ac854ffb2da6662023-12-01T22:06:57ZengMDPI AGEntropy1099-43002022-07-0124797310.3390/e24070973Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid StrategyQingyu Xia0Yuanming Ding1Ran Zhang2Huiting Zhang3Sen Li4Xingda Li5Communication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaThis paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhance the population’s diversity and ergodicity. Then, inspired by the sigmoid function, a new parameter is designed to strengthen the ability of the algorithm to coordinate early exploration and late development. Finally, the particle swarm optimization learning strategy is introduced into the seagull position updating method to improve the ability of the algorithm to jump out of local optimization. Through the simulation comparison with other algorithms on 12 benchmark test functions from different angles, the experimental results show that SPSOA is superior to other algorithms in stability, convergence accuracy, and speed. In engineering applications, SPSOA is applied to blind source separation of mixed images. The experimental results show that SPSOA can successfully realize the blind source separation of noisy mixed images and achieve higher separation performance than the compared algorithms.https://www.mdpi.com/1099-4300/24/7/973seagull optimization algorithmSobol sequencesigmoid functionparticle swarm optimizationblind source separation |
| spellingShingle | Qingyu Xia Yuanming Ding Ran Zhang Huiting Zhang Sen Li Xingda Li Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy Entropy seagull optimization algorithm Sobol sequence sigmoid function particle swarm optimization blind source separation |
| title | Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy |
| title_full | Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy |
| title_fullStr | Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy |
| title_full_unstemmed | Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy |
| title_short | Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy |
| title_sort | optimal performance and application for seagull optimization algorithm using a hybrid strategy |
| topic | seagull optimization algorithm Sobol sequence sigmoid function particle swarm optimization blind source separation |
| url | https://www.mdpi.com/1099-4300/24/7/973 |
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