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.

Swarm intelligence in the problems of detecting anomalies and states of natural-technical systems and objects

An approach to solving the problem of detecting and classifying anomalies and states of natural-technical systems and objects using swarm intelligence methods is considered. The main directions of development of the proposed approach include ant algorithms, bee swarm algorithms, and the particle swarm method. The structure of the swarm intelligence system of decision support based on collective preference rules is proposed. The application of the proposed approach makes it possible to optimize the processes of processing, analysis, integration of heterogeneous data, to increase the sensitivity, reliability and efficiency of decisions made.

Ant Algorithms for Routing in Wireless Multi-Hop Networks

Wireless Multi-Hop Networks (such as Mobile Ad hoc Networks, Wireless Sensor Networks, and Wireless Mesh Networks) promise improved flexibility, reliability, and performance compared to conventional Wireless Local Area Networks (WLAN) or sensor installations. They can be deployed quickly to provide network connectivity in areas without existing backbone/back-haul infrastructure, such as disaster areas, impassable terrain, or underserved communities. Due to their distributed nature, routing algorithms for these types of networks have to be self-organized. Ant routing is a bio-inspired self-organized method for routing, which is a promising approach for routing in such Wireless Multi-Hop Networks. This chapter provides an introduction to Wireless Multi-Hop Networks, their specific challenges, and an overview of the ant algorithms available for routing in such networks.

Ant Decision Systems for Combinatorial Optimization with Binary Constraints

In this paper is considered a problem (P) which consists in minimizing an objective function f while satisfying a set of binary constraints. Function f consists in minimizing the number of constraints violations. Problem (P) is NP-hard and has many applications in various fields (e.g., graph coloring, frequency assignment, satellite range scheduling). On the contrary to exact methods, metaheuristics are appropriate algorithms to tackle medium and large sized instances of (P). A specific type of ant metaheuristics is designed to tackle (P), where in contrast with state-of-the-art ant algorithms, an ant is a decision helper and not a constructive procedure.

On the Resilience of Ant Algorithms. Experiment with Adapted MMAS on TSP

This paper focuses on the resilience of a nature-inspired class of algorithms. The issues related to resilience fall under a very wide umbrella. The uncertainties that we face in the world require the need of resilient systems in all domains. Software resilience is certainly of critical importance, due to the presence of software applications which are embedded in numerous operational and strategic systems. For Ant Colony Optimization (ACO), one of the most successful heuristic methods inspired by the communication processes in entomology, performance and convergence issues have been intensively studied by the scientific community. Our approach addresses the resilience of MAX–MIN Ant System (MMAS), one of the most efficient ACO algorithms, when studied in relation with Traveling Salesman Problem (TSP). We introduce a set of parameters that allow the management of real-life situations, such as imprecise or missing data and disturbances in the regular computing process. Several metrics are involved, and a statistical analysis is performed. The resilience of the adapted MMAS is analyzed and discussed. A broad outline on future research directions is given in connection with new trends concerning the design of resilient systems.

Ant Colony Optimisation for Performing Computational Task in Cellular Automata

A method is presented for the design of cellular automata rules by means of ant algorithms. In particular, Elitist Ant System and a~modified MAX-MIN Ant System are applied to search for transition functions of 1D cellular automata that are able to calculate squares of given input values. It will be shown that the proposed MAX-MIN Ant System can perform significantly better than the standard variant of Elitist Ant System. In particular, in the most advanced case study, the ant algorithm showed an ability to design a~complete set of elementary cellular automata rules that fulfil the required square calculations. Some selected results will be presented and their features discussed.

Rationalization of the goods flow in a transport network with the use of ant systems

The article is devoted to the problem of the flow of goods in the transport network. Ante algorithms have been used for this purpose. An optimization model based on the problem (Quadratic Assignment Problem - QAS) was built. Eight different ant algorithms were select-ed. The results of the application of ant algorithms for solving the problem of the flow of goods rationalization are presented and dis-cussed..

The shortest route search with ant algorithms

The presented research results indicate the usefulness of ant algorithms in the optimization of the road length. Admittedly, ant algorithms do not guarantee an optimal solution, but they provide a suboptimal (rational) solution. In a situation where we have limited time to solve the optimization problem, the guarantee of terminating the algorithm and obtaining a rational, acceptable solution is an attractive alternative for conducting long-term calculations and finding a little better solution.