Ant Colony Optimization Algorithms for Shortest Path Problems - Java implementation

Tato diplomová práce se zabývá hledáním nejkratší cesty pomocí mravenčích algoritmů. V teoretické části jsou popsány mravenčí algoritmy. V praktické části jsou zvoleny tyto algoritmy pro návrh a implementaci hledání nejkratší cesty v jazyce Java.This diploma thesis deals with ant colony optimization for shortest path problems. In the theoretical part it describes Ant Colony Optimization. In the practical part ant colony optimization algorithms are selected for the design and implementation of shortest path problems in the Java.

Towards a multilevel ant colony optimization

Masteroppgave i Informasjons- og kommunikasjonsteknologi IKT590 Universitetet i Agder 2014Ant colony optimization is a metaheuristic approach for solving combinatorial optimization problems which belongs to swarm intelligence techniques. Ant colony optimization algorithms are one of the most successful strands of swarm intelligence which has already shown very good performance in many combinatorial problems and for some real applications. This thesis introduces a new multilevel approach for ant colony optimization to solve the NP-hard problems shortest path and traveling salesman. We have reviewed different elements of multilevel algorithm which helped us in construction of our proposed multilevel ant colony optimization solution. We for comparison purposes implemented our own multi-threaded variant Dijkstra for solving shortest path to compare it with single level and multilevel ant colony optimization and reviewed different techniques such as genetic algorithms and Dijkstra’s algorithm. Our proposed multilevel ant colony optimization was developed based on the single level ant colony optimization which we both implemented. We have applied the novel multilevel ant colony optimization to solve the shortest path and traveling salesman problem. We show that the multilevel variant of ant colony optimization outperforms single level. The experimental results conducted demonstrate the overall performance of multilevel in comparison to the single level ant colony optimization, displaying a vast improvement when employing a multilevel approach in contrast to the classical single level approach. These results gave us a better understanding of the problems and provide indications for further research

Convergence results for continuous-time dynamics arising in ant colony optimization

This paper studies the asymptotic behavior of several continuous-time dynamical systems which are analogs of ant colony optimization algorithms that solve shortest path problems. Local asymptotic stability of the equilibrium corresponding to the shortest path is shown under mild assumptions. A complete study is given for a recently proposed model called EigenAnt: global asymptotic stability is shown, and the speed of convergence is calculated explicitly and shown to be proportional to the difference between the reciprocals of the second shortest and the shortest paths.Comment: A short version of this paper was published in the preprints of the 19th World Congress of the International Federation of Automatic Control, Cape Town, South Africa, 24-29 August 201

Hybrid Swarm Algorithm for Mobile Robot Path Planning

           The adoption of lightweight and effective swarm algorithms is required for low resource usage algorithms for mobile robot path planning crises. We present a hybrid swarm approach in this study that combines the best features of particle swarm optimization and river formation dynamics. This method looks for the shortest route while keeping the path as smooth as feasible. The best qualities of both approaches are combined and leveraged by the hybrid RFD-PSO methodology. While the RFD algorithm is well known for its smooth path discovery, it needs a lot of drops for good convergence and suffers from sinuosity problems. The generated hybrid RFD-PSO algorithm synergistically balances PSO's fast convergence with the river method's adaptive exploration and exploitation. Comparing the simulation results of the proposed method versus the Ant Colony Optimization (ACO), modified Ant Colony Optimization ACO*, PSO, RFD, A*, and Dijkstra’s, Hybrid RFD-PSO have better results in creating optimal path

Multiple Feasible Paths in Ant Colony Algorithm for mobile Ad-hoc Networks with Minimum Overhead

Mobile ad-hoc networks are infrastructure-less networks consisting of wireless, possibly mobile nodes which are organized in peer-to-peer and autonomous fashion. The highly dynamic topology, limited bandwidth availability and energy constraints make the routing problem a challenging one. Ant colony optimization (ACO) is a population based meta-heuristic for combinatorial optimization problems such as communication network routing problem. In real life, ants drop some kind of chemical substances to mark the path that they used. Then on their way, back they choose the path with the highest pheromones which becomes the shortest path. But Ant net Algorithms may cause the network congestion and stagnation. Here, multiple optimal paths are proposed with negligible overhead in spite of single optimal path in Ant net routing algorithm, so that the problem of stagnation can be rectified. This paper proposes an improved Multiple Feasible Paths in Ant Colony Algorithm for mobile Ad-hoc Networks with Minimum Overhead

An Experiment of Ant Algorithms : Case Study of Kota Kinabalu Central Town

Shortest path is one of the optimization problems that are difficult to solve. There are many algorithms that used to solve this problem. In this study, ant algorithms are used to find the shortest path using a real data. Kota Kinabalu Central Town (KKCT) is been used as the real data, where the nodes represent as buildings, the arc represent as roads and weight on the arc represent as distance. The objectives of this study are to explore and evaluate the Ant System (AS) algorithm and Ant Colony System (ACS) algorithm in finding shortest paths. Both algorithms are compared. Simulation is used as a method in this study. This is because a simulator is been designed. There are several experiment carry out using the simulator. The experiments involved manipulating several parameters. As a result, the AS was found to be not suitable for the real data used because KKCT is a graph without Hamiltonian cycle. ACS was found to be suitable for KKCT real data and produced an optimal solution

ACO Based Shortest Path between Locations within a Campus

Modelling the behaviour of nature is the most inspiring field among researchers. Many algorithms are developed based on the behaviour of nature to solve problems including solution to very complex problems. Ant Colony Optimization - ACO is a best optimization algorithm used almost in all the fields to find solution to the difficult combinatorial problems. In our paper we are using ACO to find out the shortest path between the locations within the campus. This is used to give path direction to the visually challenged students within our campus to reach out the various places. The shortest path information will be used as a basis for the design of a voice monitoring system. This voice monitoring system guides the visually challenged students