Fast chaotic optimization algorithm based on locally averaged strategy and multifold chaotic attractor

Recently, chaos theory has been used in the development of novel techniques for global optimization, and particularly, in the specification of chaos optimization algorithms (COAs) based on the use of numerical sequences generated by means of chaotic map. In this paper, we present an improved chaotic optimization algorithm using a new two-dimensional discrete multifold mapping for optimizing nonlinear functions (ICOMM). The proposed method is a powerful optimization technique, which is demonstrated when three nonlinear functions of reference are minimized using the proposed technique.