Teaching and learning best Differential Evoltuion with self adaptation for real parameter optimization

Differential Evolution (DE), a rather new members of the evolutionary computing paradigm, has rose to widespread popularity in a short time interval owing to its simple design and robust performance in a wide variety of problems. Inspired from the effect of the teacher on the knowledge gained by the learners in a class, we propose a teaching and learning based self-adaptive DE (TLBSaDE) for solving real parameter optimization problems. A benchmark suite consisting of 28 test problems, proposed for the CEC 2013 Special Session and Competition on Real Parameter Optimization, is used. The results are obtained on 10, 30 and 50 dimensional problems.