A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization

TL;DR

This paper introduces a parallel divide-and-conquer evolutionary algorithm designed for large-scale optimization problems, effectively leveraging parallel computing to improve solution quality and efficiency.

Contribution

It presents a novel DC-based EA that is compatible with parallel computing without sacrificing solution quality, addressing limitations of existing methods.

Findings

Achieves high-quality solutions for large-scale problems

Effectively utilizes parallel computing resources

Outperforms existing DC-based EAs in solution quality

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 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.