An Inertial Grouping and Overlapping Feature Selection Assisted Algorithm for Expensive Large-scale Optimization Problems.

Challenges in expensive large-scale optimization problems, such as high coupling between variables, easy falling into local optimal solution, and computationally expensive objective function, resulted in the difficulty to achieve the global optimal solution. An inertial grouping and overlapping feature selection technique for cooperative revolutionary (IG-OFSA) algorithms was proposed to solve expensive large-scale optimization problems. In the proposed algorithm, firstly, a large-scale optimization problem was decomposed into several low-dimensional overlapping sub-problems by using overlapping feature selection technology, and each sub-problem was optimized in-dependently with the assistance of a surrogate model. Then, promising solutions found for each sub-problem would be merged into a context vector for expensive objective evaluation. In addition, an inertial grouping technology was used to control the frequency of regrouping during the optimization to extend the cycle of exploitation of the grouping scheme, and correspondingly improved the performance of optimization. The performance of IG-OFSA was tested on 15 CEC2013 benchmark problems and compared with three state-of-the-art algorithms. The experimental results showed that the performance of IG-OFSA was competitive to solve the expensive large-scale optimization problem, especially, good for solving problems with partially separable, overlapping or completely non-separable decision variables. [ABSTRACT FROM AUTHOR]