On learning performance evaluation for some psycho-learning experimental work versus an optimal swarm intelligent system

Some models closely related to animal psycho-learning, and a swarm intelligent system, are comparatively presented. More specifically, three introduced models are inspired by creatures' behavioral learning phenomenon, observed in nature. Two of presented models based on Pavlov's and Thorndike's excremental work. Pavlov's dog learns how to associate two inputs sensory stimuli (audible, and visual signals). Thorndike's cat behavioral learning that to get out from a cage for obtaining food. Each of behavioral learning models improves its performance by minimizing response time period. Additionally, other third model motivated by ant colony system (ACS). optimized performance. That model simulates a swarm (ant) intelligent system used for solving optimally traveling salesman problem (TSP). That by bringing food from different food sources to store (in cycles) at ant's nest. Moreover, three other learning models based on pulsed neurons criterion, parallel genetic algorithmic programming, and modified Hebbian learning paradigm (Oja's rule). Interestingly, those models shown to behave analogously to previously suggested: Pavlov's, Thorndike's and ACS models