Convergence analysis for optimizing course schedule problems (CSP) at high school using the hybrid artificial bee colony algorithm.

The study is to optimize the course schedule problems in high school using the Hybrid Artificial Bee Colony Algorithm. The course schedule problems in high school are something that needs special attention. Creating an optimal schedule is quite difficult because there are many interrelated variables that require significant handling. To make an effective and optimal course schedule problems can be done through an optimization process. The author will use the Hybrid Artificial Bee Colony algorithm with Python software. The advantage of the Hybrid Artificial Bee Colony algorithm is that it has a very efficient optimization algorithm in solving problems, especially optimal solutions and local or global optimization. Initialization of the population is the initial stage of the hybrid artificial bee colony algorithm and then the initial solution value is calculated. Then enter the employed bee stage and then stored in the onlooker bee phase. In order to be more optimal, crossover is carried out again and produces an optimal solution. At the initial stage of the solution, a candidate solution is randomly searched. Next, the employee bee's solution is selected by the onlooker bee. After going through the crossover, the scout bee evaluates the best solution. The solution by using the Hybrid Artificial Bee Colony algorithm will be convergence analyzed to show that the course schedule problems have been well structured and optimal. [ABSTRACT FROM AUTHOR]