A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
This addresses the problem of scalability in optimization for industrial applications, offering a practical improvement over existing methods.
The paper tackles the challenge of evolutionary algorithms failing to effectively and efficiently solve large-scale optimization problems with thousands of variables by proposing a novel Divide-and-Conquer based EA that produces high-quality solutions and utilizes parallel computing without compromising solution quality.
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 efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA that can not only produce high-quality solution by solving sub-problems separately, but also highly utilizes the power of parallel computing by solving the sub-problems simultaneously. Existing DC-based EAs that were deemed 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.