Implementation of the Ant Colony System Algorithm in the Lecture Scheduling Process

Scheduling lectures using the ant algorithm is able to provide alternative solutions for scheduling lectures that want a certain time optimally. In this research, the method used is literature study. This literature study was carried out by reading books and literature related to scheduling and ant algorithms. In addition to using literature studies, researchers also use field studies. This is intended to get a true picture of the state of the institution by observing directly at the institution, by means of observation or data collection methods by conducting interviews on the parts involved in the process of preparing the class schedule. From the results of the analysis obtained: scheduling lectures using the ant algorithm is able to provide solutions in scheduling. The ant algorithm can be applied to create a class schedule with optimal results, the ant algorithm is able to provide solutions in the preparation of a lecture schedule that requires a certain time.

Ecological Security Pattern Construction in Karst Area Based on Ant Algorithm

Constructing the ecological security pattern is imperative to stabilize ecosystem services and sustainable development coordination of the social economy and ecology. This paper focuses on the Karst region in southeastern Yunnan, which is ecologically fragile. This paper selects the main types of ecosystem services and identifies the ecological source using hot spot analysis for Guangnan County. An inclusive consideration of the regional ecologic conditions and the rocky desertification formation mechanism was made. The resistance factor index system was developed to generate the basic resistance surface modified by the ecological sensitivity index. The Ant algorithm and Kernel density analysis were used to determine ecological corridor range and ecological restoration points that constructed the ecological security pattern of Guangnan County. The results demonstrated that, firstly, there were twenty-three sources in Guangnan County, with a total area of 1292.77 km2, accounting for 16.74% of the total. The forests were the chief ecological sources distributed in the non-Karst area, where Bamei Town, Yangliujing Township and Nasa Town had the highest distribution. Secondly, the revised resistance value is similar to “Zhe (Zhetu Township)-Lian (Liancheng Town)-Yang (Yangliujing Township)-Ban (Bambang Township)”. The values were lower in the north and higher in the south, which is consistent with the regional distribution of Karst. Thirdly, the constructed ecological security pattern of the “Source-Corridor-Ecological restoration point” paradigm had twenty-three ecological corridors. The chief ecological and potential corridor areas were 804.95 km2 and 621.2 km2, respectively. There are thirty-eight ecological restoration points mainly distributed in the principal ecological corridors and play a vital role in maintaining the corridor connectivity between sources. The results provide guidance and theoretical basis for the ecological security patterns construction in Karst areas, regional ecologic security protection and sustainable development promotion.

Software Model for Determining the Optimal Routes in a Computer Network Based on the Two-Colonial Ant Algorithm

Purpose. At present, the computer networks of the information and telecommunication system (ITS) of railway transport use the OSPF protocol, which does not allow taking into account several metrics when determining the optimal route. Therefore, there is a need to study the possibility of organizing routing in computer networks of rail transport ITS using a two-colonial ant algorithm. Methodology. According to the Two-ACO software model, created in the Python language based on the two-colonial ant algorithm, the optimal route in a computer network was determined. Two-ACO model inputs: computer network parameters (network adjacency matrix, number of routers); parameters of the ant algorithm (number of iterations; number of ants in the colony; number of elite ants; initial pheromone level; evaporation rate; parameter for adjusting the amount of pheromone deposition). Findings. The results of the Two-ACO model are presented in the form of graphs depicting the optimal paths: the criterion of the total delay on the routers (for the first colony of ants) and the number of hops (for the second colony of ants). Originality. According to the created Two-ACO software model for a computer network of 7 routers and 17 channels, a study of the time for determining the optimal path in a computer network by the number of ordinary and elite ants, evaporation rate and deposited pheromone was conducted. It is determined that it is enough to use the number of ants equal to the number of routers and have 2 elite ants in the colony, with 1000 iterations, evaporation rate from 0.2 to 0.7, and pheromone deposition by ants close to one. Practical value. Created Two-ACO software model using two colonies of ants on the following criteria: the total delay on the routers (for the first colony of ants) and the number of hops that make up the route (for the second colony of ants) allows you to parallel determine the optimal routes in a computer network of railway transport. It is estimated that for a computer network of 15 routers and 17 channels, it is sufficient to have 30 agents (two ants on top), the value of the pheromone deposited by the agents is close to one, and the evaporation rate is 0.4.

Penentuan Jalur Terpendek Pendistribusian Barang Jalur Nugraha Ekakurir (JNE) Menggunakan Algoritma Semut (Studi Kasus JNE Dewi Sartika Palu)

Nowasday, it is often done traveling from one place or location to another by considering the path or the shortest route to be traveled. In determining the shortest route by implementing the ant algorithm to determine the shottest route. This case is a case of the traveling salesmen problem (TPS) namely visiting all location points starting from the starting point and ending at the starting point again. Case study in this research is delivery data of JNE courier palu. The sample used is 13 points which are then resolved using the ant algorithm adopts the ant work method to get the shortest route. The use of the ant algorithm in this case is limited to one cycle or one iteration (NC=1) so that the best route for the first cycle is obtained JNE Gudang utama dewi sartika palu( JNE jalan banteng No.8a biromaru palu selatan ( JNE basuki rahmat ( ), JNE jalan anoa No.107 pau selatan ( JNE diponegoro lere palu barat( JNE emysaelan No.20 palu selatan ( JNE sisingamangaraja palu ( JNE jalan kimaja palu barat ( ), JNE krajalemba No.7 sigi biromaru ( JNE jalan tombolotutu palu JNE samratulangi No.66 A palu ( ), JNE tondo kamus mantikulore ( JNE diponegoro lere palu barat ( anddcome back to JNE gudang utama dewi sartika palu ( ) with a distance of 56.4 km.

Collective Intelligence Algorithms in Pedagogical Practice

The article considers the ideas of using artificial intelligence algorithms in pedagogics. It presents the methodology of the so-called collective pedagogical megasystem. The introduction of such an ephemeral construct is necessary only to understand the collective pedagogical intelligence system, formulate it in a model, find out its operation patterns and the laws it obeys. It would contribute to predicting pedagogical processes and phenomena and formulating new laws. The objective of the article is to demonstrate the application of collective intelligence algorithms in pedagogical practice for effective didactic decision-making. The matter is that in a real educational process, besides the well-known set of pedagogical conditions, there are some random and unpredictable reasons and factors that are hard to foresee or anticipate. Due to their stochastic nature, they occur spontaneously. These single reasons make a minor impact on the teaching methods selection, but in aggregate their influence gets so strong that they can upturn some prognostic conclusions. The problem also focuses on identifying the factors that would ensure the highest efficiency and productivity of studies among the known (expected) and random (unexpected) reasons. For these purposes, the most suitable algorithm for the selection training methods is the so-called ant algorithm which, on the one hand, considers the randomness of the influence parameters, and on the other, ensures steady and high productivity. A certain example was selected to demonstrate the process of applying the ant algorithm to reveal the best hierarchy of the pedagogical conditions (factors) that determines the optimum choice of the training method. The authors conclude that human intelligence is distributed and integrated at the same time, and the application of collective intelligence algorithms in pedagogical practice can yield some effective didactic decisions