Boosting Kernel Search Optimizer with Slime Mould Foraging Behavior for Combined Economic Emission Dispatch Problems

Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas emissions. Boosting kernel search optimizer (BKSO) is introduced in this research to solve the combined economic emission dispatch (CEED) problem. Inspired by the foraging behavior in the slime mould algorithm (SMA), the kernel matrix of the kernel search optimizer (KSO) is intensified. The proposed BKSO is superior to the standard KSO in terms of exploitation ability, robustness, and convergence rate. The CEC2013 test function is used to assess the improved KSO's performance and compared to 11 well-known optimization algorithms. BKSO performs better in statistical results and convergence curves. At the same time, BKSO achieves better fuel costs and fewer pollution emissions by testing with four real CEED cases, and the Pareto solution obtained is also better than other MAs. Based on the experimental results, BKSO has better performance than other comparable MAs and can provide more economical, robust, and cleaner solutions to CEED problems.

Automated summary

In this research, Boosting kernel search optimizer (BKSO) is introduced to solve the combined economic emission dispatch (CEED) problem. BKSO performs better than the standard KSO in terms of exploitation ability, robustness, and convergence rate. Experimental results show that BKSO outperforms other optimization algorithms in statistical results, convergence curves, fuel costs, and pollution emissions.