Ant colony algorithms are a class of metaheuristics which are inspired from the behavior of real ants. The original idea consisted in simulating the stigmergic communication, therefore these algorithms are considered as a form of adaptive memory programming. A new formalization is proposed for the design of ant colony algorithms, introducing the biological notions of heterarchy and communicatio...

-In this article two different optimization algorithms are presented to solve the deficiency of ant colony algorithm such as slow convergence rate and easy to fall into local optimum. This method based on Max-Min Ant System, established an adaptive model for pheromone evaporation coefficient adjusted adaptively and avoided the ants falling into local optimum. At the same time, this optimization...

This paper presents an ant colony optimization based algorithm to solve real parameter optimization problems. In the proposed method, an operation similar to the crossover of the genetic algorithm is introduced into the ant colony optimization. The crossover operation with Laplace distribution following a few promising descent directions (FPDD-LX) is proposed to be performed on the pheromone of...

Real world problems are often of dynamic nature. They form a class of difficult problems that metaheuristics aim to solve. The goal is not only to attempt to find near-to optimal solutions for a defined objective function, but also to track them in the search space. We will discuss in this article the dynamic optimization in the continuous case. Then we will present the experimentation on a bat...

Ant colony optimization (ACO) is a technique for optimization that was introduced in the early 1990’s. The inspiring source o f ant colony optimization is the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete and continuous optimization problems and to important problems in telecommunications, such a...

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...

The first ant colony optimization (ACO) called ant system was inspired through studying of the behavior of ants in 1991 by Macro Dorigo and co-workers [1]. An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony. Optimization problems can be solved through simulating ant’s behaviors. Since the first ant system algorithm was proposed, there is...

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...