SWOOP: An adjustable global optimization technique

Abstract

Abstract Optimization is the process of obtaining the best result under given circumstances and in its broadest sense; optimization can be applied to solve any engineering problem. In many practical applications, the objective function contains, besides the global minimum, several local minima. Implementing local optimization algorithms to functions that contain multiple local minima within the search space results in problem instability. Unfortunately, the distinction between the concepts of local and global optimization is frequently overlooked by researchers although a huge amount of work has been done in this field due to the cumbersome of implementing a global optimization technique. A recent technique for global optimization was developed under the name of Coupled Local Minimizers (CLM) in which the local minimizers exchange information with each other resulting into a cooperative search mechanism. This technique was comprehensively discussed in the realm of finite element model updating and many challenges were illustrated that hinder the practical implementation of the technique. In this paper, the authors present a modified version of the CLM technique under the name of SWOOP in some of the drawbacks the CLM are mitigated, and then added a perusal part to the technique by plotting a synthesized response surface of the function to guide the optimization process. The proposed SWOOP technique is tested for various function types and its superior performance was validated.