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.

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