Srivastava Abhishek, Das Dushmanta Kumar
IEEE Trans Cybern. 2022 Jun;52(6):4187-4197. doi: 10.1109/TCYB.2020.3024607. Epub 2022 Jun 16.
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.
优化技术被广泛用于解决电力系统中大型复杂的经济负荷调度（ELD）和联合排放经济调度（CEED）问题。这些技术能够在较短的计算时间内解决这些问题。本文提出了一种基于人类智能的新型元启发式优化技术，即强化班级尖子生优化算法（CTO），用于解决ELD和CEED问题。该算法是经典CTO的升级形式，其中融入了辅导课的概念，以提高班级中学习能力较弱学生的学习能力。为了验证所提算法的探索、利用、收敛和避免局部极小值的能力，考虑了29个基准函数。此外，还考虑了七个不同的ELD问题测试案例和四个CEED问题测试案例，以检验所提算法解决这些复杂问题的有效性。结果分析证明，所提算法在几乎每个测试案例中都能提供更好、更有效的结果。
