Economic Dispatch (ED) is a fundamental optimization problem in power system operation, often characterized by nonlinearity, non-convexity, and practical constraints such as valve-point effects (VPE), ramp-rate limits (RRL), and transmission losses (TL). This paper presents a systematic evaluation of the Hunger Games Search (HGS) algorithm for constrained economic dispatch (ED) problems under diverse operating conditions. A comprehensive and systematically controlled evaluation framework is developed, considering benchmark systems ranging from small-scale (6 units) to large-scale (up to 160 generating units). All experiments are conducted over 30 independent runs, and performance is assessed using statistical indicators including best, mean, worst, and standard deviation of fuel cost. Constraint feasibility is enforced using the hybrid strategy, with power balance error maintained below 1e-6. To ensure fair comparison, computational effort is standardized using the number of function evaluations (NFE). In addition to conventional benchmarking, a detailed parameter tuning and sensitivity analysis is performed to examine the influence Wilcoxon signed-rank test and Friedman test, are employed to rigorously validate the significance of performance differences across algorithms. The results indicate that HGS achieves generally stable performance with variability in complex cases, across a wide range of ED formulations, particularly in large-scale and highly constrained scenarios. The combined use of sensitivity analysis and statistical validation provides deeper insight into the behavior of HGS beyond outcome-based comparison. These findings demonstrate that HGS is a competitive and effective metaheuristic, with performance dependent on problem complexity and constraint severity for solving complex ED problems, while also providing a reproducible and statistically grounded framework for performance evaluation.

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