Differential Evolution - Strategies and Enhancements

Abstract

Differential Evolution (DE) is a powerful optimization algorithm known for its simplicity and effectiveness in solving complex optimization problems. This research paper provides a comprehensive review of the strategies and enhancements proposed for DE algorithms to improve their performance. We discuss various strategies, including parameter adaptation, population diversity maintenance, and hybridization with other algorithms, along with their impact on the efficiency and effectiveness of DE. Additionally, we explore enhancements such as self-adaptation, constraint handling mechanisms, and parallelization techniques to further improve the scalability and robustness of DE. Through a comparative analysis and experimental results, we highlight the strengths and weaknesses of different strategies and enhancements, providing insights into their practical implications for solving real-world optimization problems.