14. Global Sensitivity Analysis-Based Optimization Algorithm

In this chapter a single-solution metaheuristic optimizer, namely, global sensitivity analysis-based (GSAB) algorithm [1], is presented that uses a basic set of mathematical techniques, namely, global sensitivity analysis. Sensitivity analysis (SA) studies the sensitivity of the model output with respect to its input parameters (Rahman [2]). This analysis is generally categorized as local SA and global SA techniques. While local SA studies the sensitivity of the model output about variations around a specific point, the global SA considers variations of the inputs within their entire feasibility space (Pianosi and Wagener [3], Zhai et al. [4]). One important feature of the GSA is factor prioritization (FP), which aims at ranking the inputs in terms of their relative contribution to output variability. The GSAB comprises of a single-solution optimization strategy and GSA-driven procedure, where the solution is guided by ranking the decision variables using the GSA approach, resulting in an efficient and rapid search. The proposed algorithm can be studied within the family of search algorithms such as the random search (RS) by Rastrigin [5], pattern search (PS) by Hooke and Jeeves [6], and vortex search (VS) by Dog and Ölmez [7] algorithms. In this method, similar to these algorithms, the search process is achieved in the specified boundaries. Contrary to these algorithms that use different functions for decreasing the search space, in the present method, the well-known GSA approach is employed to decrease the search boundaries. The minimization of an objective function is then performed by moving these search spaces into around the best global sample.