Weighted Tree Seed Teaching–Learning-Based Optimization (WTSTLBO) Algorithm for the Static Economic Load Dispatch Problem

This study proposes a novel Weighted Tree-Seed Teaching–Learning-Based Optimization (WTSTLBO) algorithm to solve the Static Economic Load Dispatch (SELD) problem. The proposed algorithm integrates the exploration capability of the Tree-Seed Algorithm (TSA), the exploitation capability of the Teaching–Learning-Based Optimization (TLBO) algorithm, and an adaptive inertia-weight strategy to improve convergence speed and optimization accuracy. The SELD problem aims to minimize the total fuel cost while satisfying power balance, generator operating limits, transmission losses, and ramp-rate constraints. The performance of the proposed WTSTLBO algorithm is evaluated on standard IEEE 6-unit, 15-unit, and 40-unit test systems. Simulation results demonstrate that WTSTLBO outperforms the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), TSA, Weighted TSA (WTSA), TLBO, Weighted TLBO (WTLBO), and the hybrid Tree-Seed Teaching–Learning-Based Optimization (TSTLBO) algorithm in terms of fuel cost, convergence speed, and robustness. The obtained results confirm the effectiveness and superiority of the proposed algorithm for solving complex large-scale economic load dispatch problems.