COMPLEXITY AND PERFORMANCE COMPARISON OF GENETIC ALGORITHM AND ANT COLONY FOR BEST SOLUTION TIMETABLE CLASS

Ant colony and genetic algorithm are two solution methods used to determine the efficiency of the search time for a solution. This study aims to make a comparison of ant colony and genetic algorithm in terms of providing the results of the learning schedule in the department. This comparison uses the same variables to tested on ant colony and genetic algorithm. Variables used for department majors such as the number of lecturers, days, number of classes, time slots per day. This comparison also uses the asymptotic complexity method to see the asymptotic complexity of using ant colony and genetic algorithm. The results of the investigation obtained, the average genetics algorithm performance time obtained by ten tests is 0.9856s and the average number of cycles is 4.9. In ant colony performance, the average time obtained by ten tests is 6.6251s and the average number of cycles is 6.3. This result has obtained for investigation of 10 trials, with each time the experiment reaches 4 to 8 cycles. This investigation uses 96 slots with 109 available slot timetables. Asymptotic Complexity for ant colony is n(4), and genetic algorithm is n(3). A comparison of ant colony and genetic algorithm illustrates that genetic algorithm is the fastest algorithm for scheduling cases. This process gaves a best solution effect speed to get the best scheduling solution.