Data stream mining has recently emerged in response to the rapidly increasing continuous data generation. While majority of Ant Colony Optimisation (ACO) rule induction algorithms have proved be successful producing both accurate and comprehensive classification models nonstreaming (batch) settings, currently ACO-based for problems are not suited applied mining. One main challenges is iterative...

The ant colony optimisation metaheuristic has shown promise on simplified artificial instances of university course timetabling problems. However, limited work has been done applying it to practical timetabling problems. In this paper, we describe the application of the ant colony optimisation to a highly constrained real–world instance of the university course timetabling problem. We present t...

This master thesis presents an ant colony optimisation algorithm for the bounded diameter minimum spanning tree problem, a NP-hard combinatorial optimisation problem with various application fields, e.g. when considering certain aspects of quality in communication network design. The algorithm is extended with local optimisation in terms of a variable neighbourhood descent algorithm based on fo...

Manufacturing supply chain(SC) faces changing business environment and various customer demands. Pareto Ant Colony Optimisation (P-ACO) in order to obtain the non-dominated set of different SC designs was utilized as the guidance for designing manufacturing SC. PACO explores the solution space on the basis of applying the Ant Colony Optimisation algorithm and implementing more than one pheromon...

Many animals use chemical substances known as pheromones to induce behavioural changes in other members of the same species. The use of pheromones by ants in particular has lead to the development of a number of computational analogues of ant colony behaviour including Ant Colony Optimisation. Although many animals use a range of pheromones in their communication, ant algorithms have typically ...

Ant Colony Optimisation is a metaheuristic for combinatorial optimisation problems. In this paper we show its successful application to the Vehicle Routing Problem (VRP). First, we introduce VRP and its many variants, such as VRP with Time Windows, Time Dependent VRP, Dynamic VRP, VRP with Pickup and Delivery. These variants have been formulated in order to bring the VRP as close as possible to...

ant colony optimisation (aco) algorithm and adaptive refinement mechanism are used in this paper for solution of optimization problems. many of the real engineering problems are، however، of continuous nature and finding their solution by discrete ant based algorithms requires discretisation of the decision variables in which affected the convergence and performance of the algorithm. in this pa...

Ant Colony Extended (ACE) is a novel algorithm belonging to the general Ant Colony Optimisation (ACO) framework. Two specific features of ACE are: the division of tasks between two kinds of ants, namely patrollers and foragers, and the implementation of a regulation policy to control the number of each kind of ant during the searching process. In addition, ACE does not employ the construction g...

Ant colony optimisation (ACO) is a commonly used meta-heuristic to solve complex combinatorial problems like the travelling salesman problem (TSP), vehicle routing (VRP) etc. However, classical ACO algorithms provide better optimal solutions but do not reduce computation time overhead significant extent. Algorithmic speed-up can be achieved by using parallelism offered quantum computing. Existi...

The Ant Colony Optimisation Algorithm (ACO) supports the development of a system for a multi-objective network optimisation problem. The ACO system bases itself on an agent’s population and, in this case, uses a multi-level pheromone trail associated to a cost vector, which will be optimised.