Cloud Adaptive Genetic Algorithm

The Cloud Adaptive Genetic Algorithm is an intelligent optimization approach that combines cloud model theory with traditional genetic algorithms, enhancing search efficiency through dynamic parameter adjustment.

Traditional genetic algorithms often suffer from premature convergence or low search efficiency when solving complex optimization problems. The Cloud Adaptive Genetic Algorithm introduces the "uncertainty" characteristics of cloud models, primarily manifested in three aspects:

Dynamic Adjustment of Crossover and Mutation Probabilities Leveraging the fuzziness and randomness features of cloud models, the algorithm automatically adjusts crossover/mutation probabilities based on population fitness distribution. When population diversity is high, mutation probability decreases to preserve elite individuals; when trapped in local optima, mutation intensity increases to enhance escape capability. In code implementation, this typically involves calculating population diversity metrics and mapping them to probability values using cloud membership functions.

Adaptive Selection Pressure Control The algorithm dynamically regulates selection pressure through cloud generators. Strong selection pressure is maintained in early evolution stages to accelerate convergence, while reduced pressure in later stages maintains population diversity, balancing exploration and exploitation capabilities. Programmatically, this can be achieved by adjusting selection operator parameters (like tournament size or roulette wheel scaling) based on generation count and fitness variance.

Hybrid Cloud Mutation Operator Combining characteristics of normal cloud models and uniform mutation, the operator applies small-magnitude cloud mutations near optimal solutions and large mutations in distant regions, achieving synergy between global search and local fine-tuning. Implementation typically involves using cloud droplets to generate mutation steps, where the mutation magnitude follows a normal distribution centered around the current solution with adaptive variance control.

This method is particularly suitable for solving complex optimization problems with multi-peak and nonlinear characteristics, demonstrating superior performance over traditional genetic algorithms in fields like intelligent scheduling and parameter optimization. Its core advantage lies in using cloud model-based uncertain reasoning to enable autonomous parameter adjustment according to evolutionary states, significantly reducing manual parameter tuning efforts. The algorithm can be implemented using cloud digital characteristics (Ex, En, He) to control evolutionary operators dynamically throughout the optimization process.