The Combined Economic Emission Dispatch (CEED) problem is crucial for optimizing power system operation by minimizing costs and emissions while ensuring grid stability and meeting demand. This paper addresses the complex, nonlinear, and nonconvex nature of CEED, arising from factors like valve-point effects and transmission losses, which necessitates efficient metaheuristic algorithms. We introduce an Improved Zebra Optimization Algorithm (IMZOA), an enhanced bio-inspired technique integrating advanced adaptive foraging and dynamic defense mechanisms, along with a cubic function for CEED modeling, to improve search efficiency and convergence. IMZOA demonstrates significant numerical improvements, achieving up to a 0.80 % cost reduction for the six-unit system compared to the standard Zebra Optimization Algorithm (ZOA), minimizing hourly fuel cost to $69,563.04 for the Iraqi system, and exhibiting competitive performances with a fuel cost of $197,974.2047 per hour for the 110-unit system. IMZOA's effectiveness is validated on three benchmark systems: a six-unit IEEE test system, the 31-unit Iraqi power generation system, and a large-scale 110-unit system. Experimental results show that IMZOA significantly reduces costs and emissions compared to established algorithms like LM and SA, and improved methods such as asinhCAOA, RLADE, and the standard Zebra Optimization Algorithm ZOA. Specifically, for the six-unit system, IMZOA also showed superior environmental performance. For the Iraqi system, IMZOA outperformed PSO and other state-of-the-art approaches. Moreover, for the 110-unit system, IMZOA demonstrated competitive performance comparable to advanced algorithms like EBWO and ESNS, while maintaining lower standard deviations, indicating greater solution stability. These results underscore IMZOA's robustness and efficiency for small, medium, and large-scale CEED problems, making it a promising solution for cost-effective and environmentally sustainable power dispatch.
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