A Decomposition-based Approach for Constrained Large-Scale Global Optimization

Many real-world global optimization problems are too complex for comprehensive analysis and are viewed as "black-box" (BB) optimization problems. Modern BB optimization has to deal with growing dimensionality. Large-scale global optimization (LSGO) is known as a hard problem for many optimization techniques. Nevertheless, many efficient approaches have been proposed for solving LSGO problems. At the same time, LSGO does not take into account such features of real-world optimization problems as constraints. The majority of state-of-the-art techniques for LSGO are based on problem decomposition and use evolutionary algorithms as the core optimizer. In this study, we have investigated the performance of a novel decomposition-based approach for constrained LSGO (cLSGO), which combines cooperative coevolution of SHADE algorithms with the epsilon-constraint handling technique for differential evolution. We have introduced some benchmark problems for cLSGO, based on scalable separable and non-separable problems from IEEE CEC 2017 benchmark for constrained real parameter optimization. We have tested SHADE with the penalty approach, regular epsilon-SHADE and epsilon-SHADE with problem decomposition. The results of numerical experiments are presented and discussed.