Enhancing large-scale power flow optimization through an advanced coati optimization algorithm

In recent times, there has been a growing demand for effective solutions to address the optimal power flow (OPF) problem. This increased attention is driven by the necessity to ensure reliable and optimal grid operations, considering factors such as generation uncertainty and rising demand. The OPF problem is designed with specific objectives to optimize power system variables while adhering to certain constraints. Therefore, this study presents a variant of an innovative optimization approach called the coati optimization algorithm (COA), which is based on swarm intelligence. Traditional algorithms may suffer from poor exploitation and require numerous iterations to achieve the global optimum when dealing with complex problems. To address this, the developed leader coati optimization approach (LCOA), based on the superiority of the feasible solution (SF) mechanism, enhances the exploitation capability of the standard COA and avoids getting trapped in locally optimal solutions. This is achieved by implementing a leader-based mutation-selection approach during the exploitation phase in each generation of COA. To evaluate the effectiveness of the suggested approach, a performance validation process was conducted using the CEC’17 benchmark test suites, demonstrating its superiority over COA and other recent algorithms. Additionally, three test systems adhering to IEEE standards, specifically the 30-bus, 57-bus, and 118-bus systems with fifteen case studies, were analyzed. The simulation results were assessed by comparing the performance and excellence of the proposed method with other well-designed optimization studies reported in the literature. The statistical analysis and simulation results conclusively demonstrate that the proposed LCOA exhibits superior convergence, robustness, efficiency, and high-quality feasible solutions for different OPF problems compared to the original COA and its competitors.