An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies
Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weak...
| Main Authors: | , , , , , |
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| Format: | Article |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9878325/ |
| _version_ | 1811267096726732800 |
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| author | Xuan-Yu Zhang Kai-Qing Zhou Peng-Cheng Li Yin-Hong Xiang Azlan Mohd Zain Arezoo Sarkheyli-Hagele |
| author_facet | Xuan-Yu Zhang Kai-Qing Zhou Peng-Cheng Li Yin-Hong Xiang Azlan Mohd Zain Arezoo Sarkheyli-Hagele |
| author_sort | Xuan-Yu Zhang |
| collection | DOAJ |
| description | Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optima easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, the values for both accuracy and convergence speed are higher than other algorithms. The results also indicate that the ICSSOA has an outstanding ability to jump out of the local optimum. |
| first_indexed | 2024-04-12T20:56:07Z |
| format | Article |
| id | doaj.art-a89ecb53748248b48218825f72d1bae9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T20:56:07Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a89ecb53748248b48218825f72d1bae92022-12-22T03:17:00ZengIEEEIEEE Access2169-35362022-01-0110961599617910.1109/ACCESS.2022.32047989878325An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid StrategiesXuan-Yu Zhang0Kai-Qing Zhou1Peng-Cheng Li2Yin-Hong Xiang3Azlan Mohd Zain4https://orcid.org/0000-0003-2004-3289Arezoo Sarkheyli-Hagele5School of Communication and Electronic Engineering, Jishou University, Jishou, ChinaSchool of Communication and Electronic Engineering, Jishou University, Jishou, ChinaSchool of Communication and Electronic Engineering, Jishou University, Jishou, ChinaSchool of Communication and Electronic Engineering, Jishou University, Jishou, ChinaUTM Big Data Center, Universiti Teknologi Malaysia, Skudai, Johor, MalaysiaDepartment of Computer Science and Media Technology, Internet of Things and People Research Center, Malmö University, Malmö, SwedenSparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optima easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, the values for both accuracy and convergence speed are higher than other algorithms. The results also indicate that the ICSSOA has an outstanding ability to jump out of the local optimum.https://ieeexplore.ieee.org/document/9878325/Adaptive weighting modificationcubic chaos mappinglevy flightreverse learningsparrow search algorithm |
| spellingShingle | Xuan-Yu Zhang Kai-Qing Zhou Peng-Cheng Li Yin-Hong Xiang Azlan Mohd Zain Arezoo Sarkheyli-Hagele An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies IEEE Access Adaptive weighting modification cubic chaos mapping levy flight reverse learning sparrow search algorithm |
| title | An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies |
| title_full | An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies |
| title_fullStr | An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies |
| title_full_unstemmed | An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies |
| title_short | An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies |
| title_sort | improved chaos sparrow search optimization algorithm using adaptive weight modification and hybrid strategies |
| topic | Adaptive weighting modification cubic chaos mapping levy flight reverse learning sparrow search algorithm |
| url | https://ieeexplore.ieee.org/document/9878325/ |
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