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...

In the paper we present recommendation of student courses using Ant Colony Optimization (ACO). ACO is proved to be effective in solving many optimization problems, here we show that ACO also in the problem of prediction of final grades students receives on completing university courses is able to deliver good solutions. To apply ACO in any recommender system we need special problem representati...

A hybrid ant colony optimization algorithm is proposed by introducing extremal optimization localsearch algorithm to the ant colony optimization (ACO) algorithm, and is applied to multiuser detection in direct sequence ultra wideband (DS-UWB) communication system in this paper. ACO algorithms have already successfully been applied to combinatorial optimization; however, as the pheromone accumul...

To make robot avoid obstacles in 3D space, the Pheromone of Ant Colony Optimization (ACO) in Fuzzy Control Updating is put forward, the Pheromone Updating value varies with The number of iterations and the path-planning length by each ant . the improved Transition Probability Function is also proposed, which makes more sense for each ant choosing next feasible point .This paper firstly, describ...

Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes a multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving one...

Ant colony optimization (ACO) is a well known metaheuristic. In the literature it has been used for tackling many optimization problems. Often, ACO is hybridized with a local search procedure. All the solutions generated by ants (or some of them) are improved by the local search. In this paper we propose a different framework, called Quick-and-dirty ant colony optimization. It is an hybrid appr...

Wireless Sensor Networks (WSNs) have been attracting the attention of both academic and industrial research communities for the past few years. To find an optimal topology control strategy in 3-dimensional (3D) WSNs is a new challenge. In this paper, we discuss issues related to the topology control in 3D WSNs, review a 2D model for WSNs, and introduce a 3D model for WSNs. An Immunity-Based Ant...

Ant colony optimization (ACO) has previously been applied to the Minimum Weight Vertex Cover Problem with very good results. The performance of the ACO algorithm can be improved with the use of different variations of the basic Ant Colony System algorithm, like the use of Elitism, Rank based approach and the MinMax system. In this paper, we have made an analysis of effectiveness of these variat...

In this paper is introduce "flying" ants in Ant Colony Optimization (ACO). In traditional ACO algorithms the ants construct their solution regarding one step forward. In proposed ACO algorithm, the ants make their decision, regarding more than one step forward, but they include only one new element in their solutions.

In this paper Hybrid Ant Colony Optimization (HAntCO) approach in solving Multi–Skill Resource Constrained Project Scheduling Problem (MS–RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with Ant Colony Optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed, based on both th...