ABSTRACT
Optimization is an interdisciplinary area providing solutions to nonlinear, stochastic, combinatorial, and multiobjective problems. With the increasing challenges of satisfying optimization goals of current applications, there is a strong drive to improve the development of efficient optimizers. Thus it is important to identify suitable computationally intelligent algorithms for solving the challenges posed by optimization problems. Conventional classical optimizers such as linear/nonlinear programming, dynamic programming, and stochastic programming perform poorly and fail to produce improvement in the face of such challenges. This has motivated researchers to explore the use of computational intelligence (CI) techniques to augment classical methods.
