HumanCog: A Cognitive Architecture for Solving Optimization Problems

Humans seek to select the best decision for a given problem in a process that is highly efficient and often ends with success. This is due to a high-order thinking skill: metacognition, which enables humans to be successful decision makers by constantly monitoring their cognitive activities based on earlier experience. Besides this, the social aspect of metacognition helps humans in monitoring their cognitive activities based on their peers experience and knowledge. Inspired by this, we propose HumanCog: a generic 3-layer architecture for solving optimization problems. HumanCog functions in a way that mimics human cognitive and metacognitive (self as well as social) behavior. The three layers in the network are cognitive layer, metacognitive layer and social cognitive layer. These three layers interact with each other such that accurate decision is made. As an initial work, we provide a simple realization of the HumanCog referred to as HumanCog-ver1, which self-regulates decision based on best experience. The performance evaluation on CEC 2015 and 2013 benchmark problems indicates promising results.