<p>Economic load dispatch plays a crucial role in power system operations by focusing on minimizing generation costs while adhering to operational constraints. The Multiarea Economic Dispatch (MAED) problem introduces additional complexity by accounting for interconnected power networks and addressing practical constraints like transmission losses, valve-point loading effects and prohibited operating zones. This study introduces the Quasi-Oppositional Golden Jackal Optimization (QOGJO) algorithm, a novel approach designed to address the challenges of the Golden Jackal Optimization (GJO) algorithm, tailored to address its limitations in avoiding local optima in complex scenarios. By integrating a quasi-oppositional learning strategy, QOGJO enhances global exploration and exploitation capabilities. The QOGJO performance was evaluated on three benchmark test cases: a 40-unit two-area system, a 40-unit four-area system, and a 120-unit two-area system. Compared to GJO and other state-of-the-art methods, QOGJO consistently achieves superior results, reducing generation costs while effectively handling real-world constraints. The proposed method achieves 5.29 $/h, 51.74 $/h, and 23.18 $/h better fuel cost than the original GJO algorithm for test cases 1, 2, and 3, respectively. These resutls highlight the robustness and effectiveness of the algorithm in solving the MAED problem, making it a promising tool for real-time optimization in power systems.</p>