arXiv:2611.09167 · v1
OMCDE: Opposition-based Multi-Stage Matrix-Based Competitive Differential Evolution
摘要
Differential Evolution has proven to be an effective algorithm for continuous global optimization, but it's modern high-performing variants often introduce additional complexity and computational cost. We propose OMCDE, a matrix-based competitive Differential Evolution algorithm that aims to improve optimization performance while maintaining computational efficiency. The proposed approach integrates competition-driven search dynamics, adaptive behavior between optimization stages, and opposition-based initialization within a parallelizable framework. Empirical results on widely used CEC 2017 functions show that OMCDE achieves strong performance compared to several state-of-the-art DE algorithms, while remaining efficient and scalable. These findings indicate that OMCDE is a promising alternative for solving complex numerical optimization problems.
作者备注：7 pages, 1 figures. Published in the 2026 IEEE Congress on Evolutionary Computation (CEC), IEEE WCCI 2026
期刊信息：2026 IEEE Congress on Evolutionary Computation (CEC), IEEE World Congress on Computational Intelligence (WCCI), IEEE, 2026