The Ant Colony Optimization (ACO) metaheuristic is a bio-inspired approach for hard combinatorial optimization problems for stationary and non-stationary environments. In the ACO metaheuristic, a colony of artificial ants cooperate for finding high quality solutions in a reasonable time. An interesting example of a non-stationary combinatorial optimization problem is the Multiple Elevators Prob...

Two general-purpose metaheuristic algorithms for solving multiobjective stochastic combinatorial optimization problems are introduced: SP-ACO (based on the Ant Colony Optimization paradigm) which combines the previously developed algorithms S-ACO and P-ACO, and SPSA, which extends Pareto Simulated Annealing to the stochastic case. Both approaches are tested on random instances of a TSP with tim...

Ant Colony optimization is a method to find the shortest possible path from source to sink. ACO algorithms are inspired by the social behavior of ants that use stigmergic behavior to search the food. A chemical substance left by ants, called pheromone , help them to find the food .In this paper, we present the basic behavior of ants for searching food, preliminary study of different papers rela...

Ant algorithms [18, 14, 19] are a recently developed, population-based approach which has been successfully applied to several NP-hard combinatorial optimization problems [6, 13, 17, 23, 34, 40, 49]. As the name suggests, ant algorithms have been inspired by the behavior of real ant colonies, in particular, by their foraging behavior. One of the main ideas of ant algorithms is the indirect comm...

Feature selection is an important task for data analysis and information retrieval processing, pattern classification systems, and data mining applications. It reduces the number of features by removing noisy, irrelevant and redundant data. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. Features are tr...

-Wireless Sensor Networks (WSN’s) have become an important and challenging research area in last year. Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used ...

In this paper we present an application of ant colony optimization (ACO) to the Minimum Weighted Dominating Set Problem. We introduce a heuristic for this problem that takes into account the weights of vertexes being covered and show that it is more efficient than the greedy algorithm using the standard heuristic. Further we give implementation details of ACO applied to this problem. We tested ...

In this paper we analyze various parallel implementations of the Ant Colony Optimization (ACO) applied to the Minimum Weight Vertex Cover Problem (MWVCP). We investigated the ACO algorithms applied to the MWCVP before. Here, we observe the behavior of different parallel topologies and corresponding algorithms like fully connected, replace worst, ring and independent parallel runs. We also prese...

The DBSCALE [1] algorithm is a popular algorithm in Data Mining field as it has the ability to mine the noiseless arbitrary shape Clusters in an elegant way. Such metaheuristic algorithms include Ant Colony Optimization Algorithms, Particle Swarm Optimizations and Genetic Algorithm has received increasing attention in recent years. Ant Colony Optimization (ACO) is a technique that was introduce...

Ant colony optimization algorithm (ACO) is a heuristic bionic evolutionary system based on the population. The positive feedback of ACO helps to search the approximate optimal solution, however, it also makes it easier to get trapped in local optimal solution. In order to avoid prematurity and enhance its adaptability, this paper introduces chaos principle in ACO and proposes an adaptive chaoti...