Intelligent AVR Control Using Hybrid Optimization Based On Bacterial Foragiung and Clonal Selection

This paper suggests novel hybrid optimization system (BF-CL) based on the bacterial foraging and clonal selection of immune system. A foraging strategy involves finding a patch of optimal condition (e.g., group of objective with conditions), deciding whether to enter it and search for optimal conditions, and when to leave the patch. There are predators and risks, energy required for optimization, and physiological constraints (sensing, memory, cognitive capabilities). Foraging scenarios can be modeled and optimal policies can be found using, for instance, dynamic programming. This approach provides us with novel hybrid model based on foraging behavior and clonal selection for a higher running time and with also a possible new connection between evolutionary forces in social foraging and distributed nongradient optimization algorithm design for global optimization over noisy surfaces for control system.