Constriction Factor Adaptive Quantum Class Topper Optimization for Economic Load Dispatch Problems

The study presents an improved quantum class topper optimization based on adaptive nature and constriction factor (CA-QCTO) to address economic load dispatch problems. CA-QCTO is an advanced version of classical class topper optimization, and the working principle is based on learning behavior and updating students' knowledge, where a quantum mechanism with an adaptive nature is used to enhance its searching capability for better exploration and exploitation. In addition, a constriction factor modification is added to improve the robustness of the convergence characteristic and reduce the selection of parameters. Economic load dispatch (ELD) in power systems is an important optimisation task to schedule the generation efficiently and minimize the operational cost. The main objective is to find the best fuel cost while fulfilling load demand considering various practical operational constraints. The proposed algorithm is tested mainly for two types of ELD problems - ELD with valve point effect and combined economic emission dispatch (CEED). The result reveals that the proposed algorithms outperform the existing methodology, showing its efficiency and robustness in handling dispatch problems while obtaining the lowest possible fuel cost and emissions.