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...

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...

This paper develops the formal framework of ant programming with the goal of gaining a deeper understanding on ant colony optimization and, more in general, on the principles underlying the use of an iterated Monte Carlo approach for the multi-stage solution of combinatorial optimization problems. Ant programming searches for the optimal policy of a multi-stage decision problem to which the ori...

This paper presents an ant colony optimization methodology for optimally clustering N objects into K clusters. The algorithm employs distributed agents which. AbstractAnt-based clustering is a biologically inspired data. Multi-ant colonies approach for clustering data that consists of some parallel.based on ant colony to solve the unsupervised clustering. Index TermsAnt colony optimization, Clu...

Hybrid algorithm is proposed to solve combinatorial optimization problem by using Ant Colony and Genetic programming algorithms. Evolutionary process of Ant Colony Optimization algorithm adapts genetic operations to enhance ant movement towards solution state. The algorithm converges to the optimal final solution, by accumulating the most effective sub-solutions.

This paper introduces the basic theory and procedure for working out solutions of ant colony genetic algorithm, describes optimization, constraints and objectives of construction project scheduling, then establishes basic model for scheduling optimization of construction project, and put forward improved ant colony genetic algorithm for solving the basic model. Performance of ant colony genetic...

This paper proposes an approximate optimization algorithm combining P systems with ant colony optimization, called ACOPS, to solve traveling salesman problems, which are well-known and extensively studied NP-complete combinatorial optimization problems. ACOPS uses the pheromone model and pheromone update rules defined by ant colony optimization algorithms, and the hierarchical membrane structur...

During the last years several ant-based techniques were involved to solve hard and complex optimization problems. The current paper is a short study about the influence of artificial ant species in solving optimization problems. There are studied the artificial Pharaoh Ants, Lasius Niger and also artificial ants with no special specificity used commonly in Ant Colony

This paper overviews origin and some applications of ant system of optimization. Ants live in colonies. The colony can be regarded as a multiagent organization where each agent performs independently as governed by goal of colony survival.. On observing the behavior of ants, Ant-Algorithm was proposed. The ant algorithms have been extensively studied by European researchers. These algorithms ha...

Ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. More recently, Parpinelli et al have applied ACO to data mining classification problems, where they introduced a classification algorithm called Ant Miner. In this paper, we present a system that combines both the proposed Improved Quickreduct algorithm for data preprocessing and ant ...