ABSTRACT
Optimization is an integral part of science and technology. The growing interest in the application of computationally intelligent bio-inspired paradigms to optimization engineering has introduced the potentials of using the state-of-the-art algorithms in several real-time case studies. Computational intelligence (CI) techniques have attracted the attention of several research engineers, decision makers, and practicing researchers in recent years for solving an unlimited number of complex real-world problems particularly related to the research area of optimization. In indecisive, uncertain, or chaotic existence of multiple decision variables, combined with complex constraints, and in presence of a turbulent environment, classical and traditional approaches are incapable of obtaining complete and satisfactory solutions for optimization problems. Therefore, new global optimization methods are required to handle these issues seriously. Such new methods are based on biological behavior, thus providing a generic, flexible, robust, and versatile framework for solving complex global optimization problems.
