Ameliorated quantum class topper optimization to solve economic load dispatch problems

This article proposes an ameliorated quantum class topper optimization (A-QCTO) to address the economic load dispatch (ELD) optimization problem in power systems. The A-QCTO emulates the learning behavior of students by employing a quantum mechanism and uses the cooperative escalation coefficient and dynamic improvement factor to enhance the exploration and exploitation abilities for faster and more efficient solutions to non-linear ELD problems. To validate the usefulness and robustness of the proposed algorithm, it is applied to two different test systems, i.e., ELD with valve point loading effect and cubic function ELD problems. Simulation outcomes of the test system are compared with well-known previously developed algorithms. The results show superiority in obtaining optimal fuel cost with a high convergence rate. The analysis shows its capability to handle different non-linear problems and efficiently distribute the generation with lower operation fuel costs, facilitating the electric distribution utilities and power system operators. Therefore, the proposed A-QCTO algorithm is a promising approach for solving non-linear ELD problems with higher convergence and desirable practical solutions.