Human meta-cognition inspired collaborative search algorithm for optimization

This paper presents a human meta-cognition inspired search based optimization algorithm, referred to as a Human Meta-cognition inspired Collaborative Search algorithm for optimization problems (HMICSO). Meta-cognition enables self-regulation and collaboration for effective learning and problem solving skills. Meta-cognition has been successfully applied in machine learning algorithms for providing better solutions. Taking an inspiration from this, we present a human meta-cognition inspired, population based collaborative search algorithm for optimization problems. In this algorithm, a group of people will move in a certain direction and choose an appropriate strategy for their new direction and position to lead them towards the optimum solution. The performance of the proposed HMICSO is evaluated using 4 benchmark test functions from the CEC2005 [23] competition. The performance is also compared with other existing search based optimization algorithms reported in the literature. The results clearly indicate better performance of HMICSO algorithm over other existing search based optimization algorithms.