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.

To solve the cloud computing resource scheduling problem in IaaS platform, a scheduling model based on ant colony algorithm was proposed. In this model, pheromone changes dynamically according to the best route searched by ants. This model automatically updates pheromones and guides ants to search the global best route. Experiment results show that the proposed model is of better ability in ene...

Regression test always take resource constraints into consideration, so how to choose an appropriate set of test cases among all is a crucial issue for regression test planning. This paper aims to utilize Ant Colony Algorithm and design a suitable prioritization process to optimize defect detection rate and performance of regression test under certain defined constraints. In addition, historica...

In this paper, we propose an ant colony optimization based on the predicted traffic for time-dependent traveling salesman problems (TDTSP), where the travel time between cities changes with time. Prediction values required for searching is assumed to be given in advance. We previously proposed a method to improve the search rate of Max-Min Ant System (MMAS) for static TSPs. In the current work,...

The aim of the paper is to investigate methods for deriving a suitable set of test paths for a software system. The design and the possible uses of the software system are modelled by a Markov Usage Model which reflects the operational distribution of the software system and is enriched by estimates of failure probabilities, losses in case of failure and testing costs. Exploiting this informati...

Ant colony optimization techniques are usually guided by pheromone and heuristic cost information when choosing the next element to add to a solution. However, while an individual element may be attractive, usually its long term consequences are neither known nor considered. For instance, a short link in a traveling salesman problem may be incorporated into an ant’s solution, yet, as a conseque...

The efficient use of modern communication networks depends on our capabilities for solving a number of demanding algorithmic problems, some of which are concerned with the allocation of network resources to individual connections. One of the basic operations in communication networks consists in establishing routes for connection requests between physically separated network endpoints that wish...

Developing metaheuristics requires in general a lot of work tuning different parameters. This paper presents a two–level algorithm to tackle this problem: an upper–level algorithm is used to determine the most appropriate set of parameters for a lower–level metaheuristic. This approach is applied to an Ant Colony Optimisation (ACO) metaheuristic that was designed to solve the Orienteering Probl...

Population-based method in which artificial ants iteratively construct candidate solutions. Solution construction is probabilistically biased by pheromone trail information, heuristic information and partial candidate solution of each ant (memory). Pheromone trails are modified during the search process to reflect collective experience.

Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has empirically shown its effectiveness in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). Still, very little theory is available to explain the reasons underlying ACO’s success. An ACO alternative called Omicron ACO (OA), first designed a...