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

Aanchal; Parvesh Kumar; Sarika Jain; Rekha Rani

doi:10.67334/cds31202633

Authors

Aanchal Department of Applied Mathematics, Amity University Haryana, Gurugram, India Author https://orcid.org/0009-0008-2328-4356
Parvesh Kumar Department of Mathematics, Rajkiya Engineering College, Bijnor, India Author https://orcid.org/0009-0002-9067-4318
Sarika Jain Department of Applied Mathematics, Amity University Haryana, Gurugram, India Author https://orcid.org/0000-0002-4795-9556
Rekha Rani Department of Applied Mathematics, Amity University Haryana, Gurugram, India Author https://orcid.org/0009-0004-0526-4694

DOI:

https://doi.org/10.67334/cds31202633

Keywords:

WTSTLBO, Tree-Seed Algorithm, TSA, Teaching–Learning-Based Optimization, TLBO, Static Economic Load Dispatch, Metaheuristic Optimization, Power System Optimization, Economic Dispatch

Abstract

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.

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