Economic Emission Dispatch Employing a Novel Improved Multi-Objective Artificial Lemming Algorithm
TL;DR
Results demonstrate that the IMOALA achieves superior convergence precision and solution diversity in numerical tests, offering a highly effective optimization tool for complex, multi-objective power system dispatch and broader engineering challenges.
Abstract
Multi-objective optimization algorithms are essential for solving complex engineering problems. However, conventional approaches often struggle with limited convergence accuracy and poor solution diversity. This paper proposes the Improved Multi-objective Artificial Lemming Algorithm (IMOALA), incorporating three novel components: an elite selection strategy, a differential-guided external archiving strategy, and a non-uniform mutation strategy. The performance of the IMOALA is benchmarked against other algorithms across twelve test functions and further validated on IEEE 30-bus and 39-bus systems to solve environmental economic dispatch that balances minimal fuel cost and pollutant emission. Results across Inverted Generational Distance (IGD), Maximum Spread (MS), and Generational Distance (GD) metrics demonstrate that the IMOALA achieves superior convergence precision and solution diversity in numerical tests. In engineering applications focusing on fuel cost and emission reduction, the IMOALA consistently yielded higher Normalized Distance (ND) and lower Spacing (SP) values compared to mainstream competitors, delivering evenly distributed Pareto trade-off solutions for cost–emission coordination. These findings verify the feasibility, robustness, and superiority of the IMOALA, offering a highly effective optimization tool for complex, multi-objective power system dispatch and broader engineering challenges.