Ant colony optimization is an ant algorithm framework that took inspiration from foraging behavior of ant colonies. Indeed, ACO algorithms use a chemical communication, represented by pheromone trails, to build good solutions. However, ants involve different communication channels to interact. Thus, this paper introduces the acoustic communication between ants while they are foraging. This proc...

The clustering algorithms have evolved over the last decade. With the continuous success of natural inspired algorithms in solving many engineering problems, it is imperative to scrutinize the success of these methods applied to data clustering. These naturally inspired algorithms are mainly stochastic search and optimization techniques, guided by the principles of collective behavior and self-...

In this study, we propose an optimization method by the cooperative mechanism of ant and aphid as a new Ant Colony Optimization (ACO). This algorithm is named Ant Colony Optimization with Cooperative Aphid (ACOCA). In ACOCA algorithm, the aphid searches neighborhood solutions. This solution information is treated as a honey obtained from the aphid and the honey affects the search of ACO. Moreov...

The Ant Colony Algorithm is an effective method for solving combinatorial optimization problems. However, in practical applications, there also exist issues such as slow convergence speed and easy to fall into local extremum. This paper proposes an improved Quantum Ant Colony Algorithm based on Bloch coordinates by combining Quantum Evolutionary Algorithm with Ant Colony Algorithm. In this algo...

Growing numbers of web services that offer identical functionality but differ in non-functional properties are emerging on the network, to the need to select them to form a composite service to meet user’s requirements has become one research hotspot. Web service selection methods are an attempt to to find optimal solutions for users. However, because each user’s personal preference is differen...

Ant colony algorithm of the traditional combinative optimization consumes a large amount of time in the process of solving the optimization, which has a tendency to partial optimization and slow convergence along with many redundant useless iterative codes and low operation efficiency. A generic optimized ant colony algorithm is thus proposed. This algorithm has the ability to fast global searc...

As a distributed parallel computing, cloud computing has an absolute advantage in accessing and processing of huge amount of data. How to assign all these virtual cloud computing resources to the user is a key technical issues, scholars have proposed greedy algorithm, FCFS, and other variety of algorithms to solve this problem. However, the algorithms just build a local optimal solution, there ...

An adaptive parallel ant colony optimization is presented by improving the critical factor influencing the performance of the parallel algorithm. We propose two different strategies for information exchange between processors: selection based on sorting and on difference, which make each processor choose another processor to communicate and update the pheromone adaptively. In order to increase ...