A stochastic optimization called split-compete optimization and its utilization on economic load dispatch problem

• Main Authors: Daru Kusuma, Purba, Candrasyah Hasibuan, Faisal
• Other Authors: Telkom University
• Format: Article in Journal/Newspaper
• Language: English
• Published: Institute of Advanced Engineering and Science 2025
• Subjects: Computing; Constrained optimization; Economic load dispatch; High-dimensional optimization; Metaheuristic; Random motion; Split-compete optimization; Stochastic process; Northern Goshawk; walrus*
• Online Access: https://beei.org/index.php/EEI/article/view/8665; https://doi.org/10.11591/eei.v14i2.8665

The economic load dispatch (ELD) problem is one crucial optimization problem in a power system. Metaheuristics has become a standard method to tackle this problem. Ironically, ELD is not popular enough to become a constrained use case in most studies introducing new metaheuristics, where four designs in mechanical engineering have become the most popular ones. This work introduced a new metaheuristic called split-compete optimization (SCO). It contains three serial steps where two directed searches are employed and competed against each other in each step. SCO is then assessed to tackle both unconstrained and constrained problems where 23 classic functions represent the unconstrained problems and two cases in ELD problems represent the constrained ones. Five new metaheuristics are chosen as competitors, including the coati optimization algorithm (COA), language education optimization (LEO), northern goshawk optimization (NGO), Kookaburra optimization algorithm (KOA), and walrus optimization algorithm (WaOA). In the first evaluation, SCO is superior enough by being better than COA, LEO, NGO, KOA, and WaOA in 14, 13, 20, 15, and 10 functions, whereas SCO is dominant in high-dimensional functions. Meanwhile, SCO is competitive in solving both cases of ELD problems.