Estimation of Turkey's transportation energy demand using artificial bee colony algorithm developed with the adaptive selection method

This study aimed to develop a new modified artificial bee colony (M-ABC) algorithm that can adaptively select an appropriate search equation to estimate transport energy demand (TED) more accurately. Also, M-ABC and canonical artificial bee colony (C-ABC) algorithms were compared in terms of efficiency and performance. The input parameters used in the proposed TED model were the official economic indicators of Turkey, including gross domestic product (GDP), population, and total vehicle kilometer per year (TKM). Three mathematical models, linear (M-ABCL), exponential (M-ABCE), and quadratic (M-ABCQ) were developed and tested. In the test results made with the training data set, the successful model was the exponential model. In the measurements made with the test data set, the linear and quadratic models were the models that made predictions with fewer errors. Also, economic variables were generated using the "curve fitting" technique to see TED's projections for the year 2034, under two different scenarios. In the first scenario, the results of linear, exponential, and quadratic models according to 2034 TED estimates were 40.1, 31.6, and 70.5 million tons of oil equivalent (Mtep), respectively. In the second scenario, the results of linear, exponential, and quadratic models according to the TED estimates for 2034 were found as 40.0, 31.5, and 66.5 Mtep, respectively. The presented models, M-ABCL, M-ABCE, M-ABCQ for the solution of the TED problem, produced successful results compared to the studies in the literature.