In this paper, an algorithm based on ant colony optimization for community detection from bipartite networks is presented. The algorithm establishes a model graph for the ants’ searching. Each ant chooses its path according to the pheromone and heuristic information on each edge to construct a solution. Experimental results show that our algorithm can not only accurately identify the number of ...

Constructive metaheuristics explore a tree of constructive decisions, the topology of which is determined by the way solutions are represented and constructed. Some solution representations allow particular solutions to be reached on a greater number of paths in this construction tree than other solutions, which can introduce a bias to the search. A bias can also be introduced by the topology o...

This study proposes an Ant Colony Optimization using Genetic Information (GIACO). The GIACO algorithm combines Ant Colony Optimization (ACO) with Genetic Algorithm (GA). GIACO searches solutions by using the pheromone of ACO and the genetic information of GA. In addition, two kinds of ants coexist: intelligent ant and dull ant. The dull ant is caused by the mutation and cannot trail the pheromo...

As the ant colony algorithm has the defects in robot optimization path planning such as that low convergence cause local optimum, an improved ant colony algorithm is proposed to apply to the planning of path finding for robot. This algorithm uses the search way of exhumation ant to realize the complementation of advantages and accelerate the convergence of algorithm. The experimental result sho...

Constraint Cellular ant algorithm is a new optimization method for solving real problems by using both constraints method, the evolutionary rule of cellular, graph theory and the characteristics of ant colony optimization. Multi-objective vehicle routing problem is very important and practical in logistic research fields, but it is difficult to model and solve because objectives have complicate...

In this paper, thinking over characteristics of ant colony optimization Algorithm, taking into account the characteristics of cloud computing, combined with clonal selection algorithm (CSA) global optimum advantage of the convergence of the clonal selection algorithm (CSA) into every ACO iteration, speeding up the convergence rate, and the introduction of reverse mutation strategy, ant colony o...

Investment decision making is one of the key issues in financial management. Selecting the appropriate tools and techniques that can make optimal portfolio is one of the main objectives of the investment world. This study tries to optimize the decision making in stock selection or the optimization of the portfolio by means of the artificial colony of honey bee algorithm. To determine the effect...

Ant colony optimization algorithm is a heuristic approach for the solution of combinatorial optimization problems. In order to solve continuous optimization models, an ant colony optimization algorithm is designed. Based on this algorithm, two hybrid intelligent algorithms combined with fuzzy simulation and neural network or integral sum approximation are introduced for solving fuzzy expected v...