Application of adaptive artificial bee colony algorithm in environmental and economic dispatching management

With the increasingly serious global environmental and energy issues, more countries are using environmental and economic dispatch (EED) models to optimize power systems. To better optimize the power system, an adaptive artificial bee colony (ABC) algorithm based on memory feedback mechanism was put forward to address the EED model, and the adaptive algorithm was used to adaptively adjust the population size. The study also used a benchmark function to set an appropriate population size. In addition, the study also considered both fuel cost and environmental factors in the model, and simultaneously considered four constraint conditions. To evidence the function of the adaptive algorithm, different algorithms were compared in the study. The outcomes denoted that the minimum values of the optimal solution under the Sphere function, Matyas function, and Dixon Price function were 1 x 10-273, 1 x 10-162, and 1 x 10-16, respectively, and their corresponding population sizes were 7, 18, and 20. Under the Sphere function, the minimum average fitness values of the algorithm designed by the research, the ABC algorithm, and the current optimal ABC algorithm were 10-15, 10-4, and 10-11, respectively. Moreover, the algorithm designed by the research tended to flatten out after nearly 30 iterations. The total cost of the adaptive algorithm, ABC algorithm, and the optimal algorithm was 102126.0573 yuan, 113001.0383 yuan, and 109594.9634 yuan, respectively. The pollutant emissions of the three algorithms were 1246.1048 yuan, 1250.5744 yuan, and 1344.3922 yuan, respectively. The adaptive algorithm based on memory feedback mechanism had obvious advantages in solving EED models. The adaptive algorithm proposed by the research achieved adaptive adjustment of population size, improved the operational efficiency of the algorithm, and had certain reference significance for solving other problems.