Ant Colony Optimization (ACO) is an evolutionary optimization algorithm inspired by the natural ability of colonies of ants to find the shortest path between their nest and food places by pheromone trail (Arnaout, 2013; Dorigo & Gambardella, 1997). In ACO algorithm, ants communicate information among themselves by leaving a trail using a chemical substance called pheromone. This chapter introdu...

In this paper we study a parallel implementation of the D-Ant algorithm developed by Reimann, Doerner and Hartl [9] for solving the Vehicle Routing Problem. The main idea in this algorithm is to speed up the search by letting the ants solve only sub-problems rather than the whole problem. This algorithm is well suited for parallelization. We propose a mixed parallelization strategy which combin...

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 ...

Human being is the most intelligent animal in this world. Intuitively, optimization algorithm inspired by human being creative problem solving process should be superior to the optimization algorithms inspired by collective behavior of insects like ants, bee, etc. In this paper, we introduce a novel brain storm optimization algorithm, which was inspired by the human brainstorming process. Two b...

Ant Colony Optimization (ACO) is a meta-heuristic iterative algorithm used to solve different combinatorial optimization problems. In this method, a number of artificial ants build solutions for an optimization problem and exchange information on their quality through a communication scheme that is similar to the one adopted by real ants. In this paper, Ant Colony Optimization is used to solve ...

Ant colony optimization algorithms are currently among the best performing algorithms for the quadratic assignment problem. These algorithms contain two main search procedures: solution construction by artificial ants and local search to improve the solutions constructed by the ants. Incremental local search is an approach that consists in reoptimizing partial solutions by a local search algori...

This research presents an application of Ant colony optimization meta-heuristic to the bin packing problems. Hybridization of Ant tuning strategy has introduced to improve the results obtained from basic ant system by utilizing the behaviour of artificial ants to perform local search. The objective is to pack the arbitrary sized three dimensional rectangular prismatic bins into the container of...

Fuzzy Ant-Miner algorithm processes data with nominal class and has the disadvantage of not treating the data with continuous class. In this paper, after presenting the Fuzzy Ant Miner algorithm, the authors propose a new learning method to partition heterogeneous data with continuous class. This method in a first step finds the optimal path between the data using algorithms of ants. Distance a...

Antnet is a software agent based routing algorithm that is influenced by the unsophisticated and individual ants emergent behaviour. Ants (nothing but software agents) in antnet are used to collect traffic information and to update the probabilistic distance vector routing table entries. One of the major problems with antnet is called stagnation and adaptability. This occurs, when the network f...

In this paper, we introduce a new method to solve the unsupervised clustering problem, based on a modelling of the chemical recognition system of ants. This system allow ants to discriminate between nestmates and intruders, and thus to create homogeneous groups of individuals sharing a similar odor by continuously exchanging chemical cues. This phenomenon, known as ”colonial closure”, inspired ...