The Economic Dispatch Problem (EDP) represents a fundamental optimization challenge in power system operations, aiming to minimize total generation costs while satisfying load demand and operational constraints. This paper proposes an Improved Slim Mould Algorithm (ISMA) to solve the EDP with enhanced convergence characteristics and solution quality. The ISMA incorporates adaptive parameter tuning, opposition-based learning, and local search mechanisms to overcome the limitations of the original Slim Mould Algorithm (SMA). Comprehensive computational experiments are conducted on standard IEEE test systems including 6-unit, 10-unit, and 40-unit benchmark systems. The proposed ISMA is compared against six state-of-the-art metaheuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). Results demonstrate that ISMA achieves superior performance with average cost reductions of 0.3% to 1.2% compared to competitive methods, faster convergence rates, and improved solution reliability across all test cases. Statistical analysis using Wilcoxon signed-rank tests confirms the significant superiority of ISMA at 95% confidence level.