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A novel population-based metaheuristic algorithm inspired by chaotic dynamics, called chaotic evolution optimization (CEO), is proposed. The main inspiration for CEO is derived from the chaotic evolution process of a two-dimensional discrete memristive map. By leveraging the hyperchaotic properties of the memristive map, the CEO algorithm is mathematically modeled to introduce random search directions for evolutionary processes. Then, the CEO is developed by integrating the crossover and mutation operations from the differential evolution (DE) framework.
Cita come
Yingchao (2026). Chaotic evolution optimization (https://it.mathworks.com/matlabcentral/fileexchange/183362-chaotic-evolution-optimization), MATLAB Central File Exchange. Recuperato .
Informazioni generali
- Versione 1.0.0 (4,52 KB)
Compatibilità della release di MATLAB
- Compatibile con qualsiasi release
Compatibilità della piattaforma
- Windows
- macOS
- Linux
| Versione | Pubblicato | Note della release | Action |
|---|---|---|---|
| 1.0.0 |
