Growth Optimizer for Combined Heat and Power Economic Dispatch: Performance Assessment and Comparative Analysis with Recent Nature-Inspired Algorithms

The Growth Optimizer (GO) is a new human-inspired metaheuristic algorithm reflecting human social growth processes. While it shows promise in engineering applications, its optimization behavior, convergence characteristics, exploration–exploitation balance, and scalability require further study. This paper systematically assesses GO’s performance with benchmark functions and engineering optimization problems, noting a lack of comprehensive investigation regarding its effectiveness in large-scale Combined Heat and Power Economic Dispatch (CHPED) problems. CHPED is a constrained optimization problem that seeks to minimize the total operating cost of power and heat generation while satisfying electrical and thermal demand and the operating limits of generating units. This study investigates the performance of the Growth Optimizer (GO) for solving CHPED problems with increasing system complexity. The evaluation considers three systems comprising 24, 48, and 96 generating units, with nonlinear cost characteristics and coupled heat–power operating constraints. GO is compared with four recent metaheuristic algorithms: the Artemisinin Optimization Algorithm (AOA), Blood-Sucking Leech Optimizer (BSLO), Red-Tailed Hawk (RTH) algorithm, and Red-billed Blue Magpie Optimizer (RBMO). The comparative assessment is based on solution quality, convergence behavior, and consistency across 30 independent runs. The reported results show that GO achieves minimum operating costs of $57,858.12/h, $116,095.65/h, and $233,938.48/h for the 24-, 48-, and 96-unit systems, respectively. These results demonstrate the potential of GO for handling nonlinear and nonconvex CHPED problems of different sizes. The study provides a comparative assessment of GO for coordinated electricity and heat generation scheduling and examines its suitability for complex economic dispatch applications.