Adaptive Plant Propagation Algorithm for Solving Economic Load Dispatch Problem
This addresses fuel cost optimization in power systems, but it is incremental as it adapts an existing metaheuristic approach to a known problem.
The paper tackles the Economic Load Dispatch problem by proposing an Adaptive Plant Propagation Algorithm (APPA) to optimize fuel costs under constraints, showing efficiency and robustness in tests on 3-generator and 6-generator systems with comparative analysis against APSO.
Optimization problems in design engineering are complex by nature, often because of the involvement of critical objective functions accompanied by a number of rigid constraints associated with the products involved. One such problem is Economic Load Dispatch (ED) problem which focuses on the optimization of the fuel cost while satisfying some system constraints. Classical optimization algorithms are not sufficient and also inefficient for the ED problem involving highly nonlinear, and non-convex functions both in the objective and in the constraints. This led to the development of metaheuristic optimization approaches which can solve the ED problem almost efficiently. This paper presents a novel robust plant intelligence based Adaptive Plant Propagation Algorithm (APPA) which is used to solve the classical ED problem. The application of the proposed method to the 3-generator and 6-generator systems shows the efficiency and robustness of the proposed algorithm. A comparative study with another state-of-the-art algorithm (APSO) demonstrates the quality of the solution achieved by the proposed method along with the convergence characteristics of the proposed approach.