Enhanced Polar Lights Optimization with Cryptobiosis and Differential Evolution for Global Optimization and Feature Selection
Optimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights optimization with cryptobiosis and differential evolution (CPLODE), a novel improvement upon the orig...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/1/53 |
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| author | Yang Gao Liang Cheng |
| author_facet | Yang Gao Liang Cheng |
| author_sort | Yang Gao |
| collection | DOAJ |
| description | Optimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights optimization with cryptobiosis and differential evolution (CPLODE), a novel improvement upon the original polar lights optimization (PLO) algorithm. CPLODE integrates a cryptobiosis mechanism and differential evolution (DE) operators to enhance PLO’s search capabilities. The original PLO’s particle collision strategy is replaced with DE’s mutation and crossover operators, enabling a more effective global exploration and using a dynamic crossover rate to improve convergence. Furthermore, a cryptobiosis mechanism records and reuses historically successful solutions, thereby improving the greedy selection process. The experimental results on 29 CEC 2017 benchmark functions demonstrate CPLODE’s superior performance compared to eight classical optimization algorithms, with higher average ranks and faster convergence. Moreover, CPLODE achieved competitive results in feature selection on ten real-world datasets, outperforming several well-known binary metaheuristic algorithms in classification accuracy and feature reduction. These results highlight CPLODE’s effectiveness for both global optimization and feature selection. |
| format | Article |
| id | doaj-art-5544a58e0859499e823ec25d8da9c918 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-5544a58e0859499e823ec25d8da9c9182025-01-24T13:24:44ZengMDPI AGBiomimetics2313-76732025-01-011015310.3390/biomimetics10010053Enhanced Polar Lights Optimization with Cryptobiosis and Differential Evolution for Global Optimization and Feature SelectionYang Gao0Liang Cheng1School of Petroleum Engineering, Yangtze University, Wuhan 430100, ChinaSchool of Petroleum Engineering, Yangtze University, Wuhan 430100, ChinaOptimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights optimization with cryptobiosis and differential evolution (CPLODE), a novel improvement upon the original polar lights optimization (PLO) algorithm. CPLODE integrates a cryptobiosis mechanism and differential evolution (DE) operators to enhance PLO’s search capabilities. The original PLO’s particle collision strategy is replaced with DE’s mutation and crossover operators, enabling a more effective global exploration and using a dynamic crossover rate to improve convergence. Furthermore, a cryptobiosis mechanism records and reuses historically successful solutions, thereby improving the greedy selection process. The experimental results on 29 CEC 2017 benchmark functions demonstrate CPLODE’s superior performance compared to eight classical optimization algorithms, with higher average ranks and faster convergence. Moreover, CPLODE achieved competitive results in feature selection on ten real-world datasets, outperforming several well-known binary metaheuristic algorithms in classification accuracy and feature reduction. These results highlight CPLODE’s effectiveness for both global optimization and feature selection.https://www.mdpi.com/2313-7673/10/1/53polar lights optimizationglobal optimizationfeature selectiondifferential evolutioncryptobiosis mechanismbionic algorithm |
| spellingShingle | Yang Gao Liang Cheng Enhanced Polar Lights Optimization with Cryptobiosis and Differential Evolution for Global Optimization and Feature Selection Biomimetics polar lights optimization global optimization feature selection differential evolution cryptobiosis mechanism bionic algorithm |
| title | Enhanced Polar Lights Optimization with Cryptobiosis and Differential Evolution for Global Optimization and Feature Selection |
| title_full | Enhanced Polar Lights Optimization with Cryptobiosis and Differential Evolution for Global Optimization and Feature Selection |
| title_fullStr | Enhanced Polar Lights Optimization with Cryptobiosis and Differential Evolution for Global Optimization and Feature Selection |
| title_full_unstemmed | Enhanced Polar Lights Optimization with Cryptobiosis and Differential Evolution for Global Optimization and Feature Selection |
| title_short | Enhanced Polar Lights Optimization with Cryptobiosis and Differential Evolution for Global Optimization and Feature Selection |
| title_sort | enhanced polar lights optimization with cryptobiosis and differential evolution for global optimization and feature selection |
| topic | polar lights optimization global optimization feature selection differential evolution cryptobiosis mechanism bionic algorithm |
| url | https://www.mdpi.com/2313-7673/10/1/53 |
| work_keys_str_mv | AT yanggao enhancedpolarlightsoptimizationwithcryptobiosisanddifferentialevolutionforglobaloptimizationandfeatureselection AT liangcheng enhancedpolarlightsoptimizationwithcryptobiosisanddifferentialevolutionforglobaloptimizationandfeatureselection |