Application of kernel search optimizer with composite strategy fusion to combined economic emission dispatch

Abstract The combined economic emission dispatch (CEED) problem is considered a classic non-linear, multi-objective optimization problem due to its dual objectives of reducing fossil fuel cost and pollutant emissions, and existing algorithms have been developed to solve it. However, these algorithms still exhibit certain limitations, such as poor global exploration capability, susceptibility to local optima, resulting in suboptimal outcomes in terms of both fuel cost and pollutant emissions. To overcome these challenges, this paper proposes an improved version of the original kernel search optimizer (KSO), termed the composite strategy fusion KSO (CSFKSO). CSFKSO incorporates the logistic map to enhance the distribution of the solution set and improve global search capability; it introduces a sine cosine optimization strategy to achieve a smooth transition from exploration to exploitation; and it employs a triangular walk strategy, utilizing its perturbation characteristics to enhance local exploitation capability. To validate the performance of the proposed algorithm, CSFKSO was tested on the CEC 2020 and CEC 2022 benchmark functions and compared with ten well-known optimization algorithms. The results demonstrate that CSFKSO significantly improves both global exploration and local exploitation capabilities, offering faster convergence and stronger robustness. Additionally, CSFKSO was applied to solve the CEED problem for 6, 10, 11, and 40 generator units, with results compared to those reported in existing literature. Simulation results indicate that CSFKSO effectively reduces fuel cost and pollutant emissions, providing a more competitive optimization solution for the CEED problem.