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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Biomimetics (Basel, Switzerland) Ročník 10; číslo 1; s. 53
Hlavní autoři: Gao, Yang, Cheng, Liang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 01.01.2025
MDPI
Témata:
Algorithms
bionic algorithm
Collisions (Nuclear physics)
Convergence
cryptobiosis mechanism
Datasets
differential evolution
Evolution
Exploitation
Feature selection
Genetic algorithms
global optimization
Magnetic fields
Mathematical optimization
Mutation
Optimization algorithms
polar lights optimization
differential evolution
cryptobiosis mechanism
polar lights optimization
bionic algorithm
feature selection
global optimization
ISSN:2313-7673, 2313-7673
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics10010053