Optimization techniques are widely being used to solve large and complex economical load dispatch (ELD) and combined emission economical dispatch (CEED) problems in power systems. These techniques can solve these problems in a short computational time. In this article, a new human intelligence-based metaheuristic optimization technique, that is, aggrandized class topper optimization (CTO), is proposed to solve ELD and CEED problems. This proposed algorithm is an upgraded form of classical CTO in which the concept of remedial classes is incorporated to enhance the learning ability of weak students of a class. To validate the exploration, exploitation, convergence, and local minima avoidance capabilities of the proposed algorithm, 29 benchmark functions are considered. Furthermore, seven different test cases for the ELD problem and four test cases for a CEED problem are considered to test the effectiveness of the proposed algorithm to solve these complex problems. The result analysis proves that the proposed algorithm provides better and effective results in almost each test case.