Recently, Manta ray foraging optimization (MRFO) has been developed and applied for solving few engineering optimization problems. In this paper, an elegant approach based on MRFO integrated with Gradient-Based Optimizer (GBO), named MRFO–GBO, is proposed to efficiently solve the economic emission dispatch (EED) problems. The proposed MRFO–GBO aims to reduce the probability of original MRFO to get trapped into local optima as well as accelerate the solution process. The goal of solving optimal Economic Emission Dispatch (EED) is to economically provide all required electrical loads as well as minimizing the emission with satisfying the operating equality and inequality constraints. Single and multi-objective EED problems are solved using the proposed MRFO–GBO and classical MRFO. In multi-objective EED, fuzzy set theory is adapted to determine the best compromise solution among Pareto optimal solutions. The proposed algorithm is firstly validated through well-known CEC’17 test functions, and then applied for solving several scenarios of EED problems for three electrical systems with 3-generators, 5-generators, and 6-generators. The validation is achieved through different load levels of the tested systems to prove the robustness of the proposed algorithm. The results obtained by the proposed MRFO–GBO are compared with those obtained by recently published optimization techniques as well as the original MRFO and GBO. The results illustrate the ability of the proposed MRFO–GBO in effectively solving the single and multi-objective EED problems in terms of precision, robustness, and convergence characteristics.

最近，蝠Man觅食优化（MRFO）已被开发并应用于解决一些工程优化问题。本文提出了一种基于MRFO和基于梯度的优化器（GBO）集成的优雅方法，称为MRFO–GBO，以有效解决经济排放调度（EED）问题。拟议的MRFO–GBO旨在降低原始MRFO陷入局部最优的可能性，并加快求解过程。解决最佳经济排放调度（EED）的目标是在经济上提供所有必需的电负载，并在满足运行平等和不平等约束的前提下将排放量降至最低。使用提出的MRFO–GBO和经典MRFO解决了单目标和多目标EED问题。在多目标EED中，模糊集理论适用于确定帕累托最优解中的最佳折衷解。首先通过著名的CEC'17测试函数对算法进行验证，然后将其应用于解决带有3发电机，5发电机和6发电机的三个电气系统的EED问题的几种情况。通过测试系统的不同负载水平来实现验证，以证明所提出算法的鲁棒性。拟议的MRFO–GBO获得的结果与最新发布的优化技术以及原始MRFO和GBO获得的结果进行了比较。结果说明了拟议的MRFO–GBO在精度，鲁棒性和收敛特性方面有效解决单目标和多目标EED问题的能力。