A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

Peng Yang, Ke Tang, Xin Yao

Abstract: Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas. As a powerful optimization tool for many real-world applications, evolutionary algorithms (EAs) fail to solve the emerging large-scale problems both effectively and computationally efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA that can not only produce high-quality solutions by solving sub-problems separately, but also benefits significantly from the power of parallel computing by solving the sub-problems simultaneously. Existing DC-based EAs that were thought to enjoy the same advantages of the proposed algorithm, are shown to be practically incompatible with the parallel computing scheme, unless some trade-offs are made by compromising the solution quality.