The optimization objective of Combined Economic Emissions Dispatch (CEED) must simultaneously ensure that full generation costs and pollutant emissions are minimized to meet load demands and constraints. Though the CEED problem itself has multi-objective to be optimized (e.g., fuel cost and emissions), it can be converted into a single objective with a price penalty factor. A nonlinear convergence factor-based manta ray foraging optimization algorithm was proposed for solving the CEED problems. Six nonlinear convergence functions, including the Gaussian function, sine function, cosine function, tangent function, power function and exponential function (named S1MRFO ~ S6MRFO), are introduced in the spiral foraging behavior stage based on the hypotrochoid manta ray foraging optimization algorithm (HYMRFO). These modifications enhance the MRFO algorithm's search capabilities and avoid getting trapped in local minima. The CEC-BC-2017 benchmark functions were used to examine the performance of six revised mathematical spiral feeding strategies for MRFO algorithm, and the S6MRFO algorithm with the best results was chosen. Combining the S6MRFO algorithm with other intelligent optimization algorithms, such as the Grey Wolf Optimizer (GWO), Arithmetic Optimization Algorithm (AOA), Multi-Verse Optimizer (MVO), Harris Hawk Optimization (HHO), Whale Optimization Algorithm (WOA), Sine Cosine Algorithm (SCA) and Ant Lion Optimizer (ALO) are compared together for optimal performance. Finally, the CEED cases with twenty units of 2500 MW and six units with four different loads (150 MW, 175 MW, 200 MW, and 225 MW) were solved by MRFO algorithm based on nonlinear convergence factors. The simulation results show that in all four test cases, the suggested strategy has the lowest fuel cost and the fewest hazardous emissions, where total costs are about 3% less than PSO and emissions are about 5.6% less.
