An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call Ant System. We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed

Absfrocl-In this paper, an ant colony algorithm is proposed to determine the optimal locations and ratings of capacitors io B distribution network far reactive power compensation. The approach is multilevel. Two separate tables of pheromones PTO maintained by the algorithm. Ants generate solution

Ant colony optimization is a metaheuristic approach belonging to the class of model-based search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms called the hyper-cube framework for ant colony optimization. In contrast to the usual way

In Ant Colony Optimization (ACO) artificial ants communicate by laying synthetic pheromone along the edges on their path through a decision graph. This attracts following ants so that they will likely search in the same region of the search space. The problem of how the pheromone information should

MACS-VRPTW, an Ant Colony Optimization based approach useful to solve vehicle routing problems with time windows is presented. MACS-VRPTW is organized with a hierarchy of artificial ant colonies designed to successively optimize a multiple objective function: the first colony minimizes the number

An ant colony optimization approach (ACO) for the resource-constrained project scheduling problem (RCPSP) is presented. Combinations of two pheromone evaluation methods are used by the ants to find new solutions. We tested our ACO algorithm on a set of large benchmark problems from the PSPLIB

. Ant Colony Optimization (ACO) is a new population oriented search metaphor that has been successfully applied to NP-hard combinatorial optimization problems. In this paper we discuss parallelization strategies for Ant Colony Optimization algorithms. We empirically test the most simple strategy

Abstract This paper presents a new feature subset selection algorithm based on the Ant Colony Optimization (ACO). ACO is a metaheuristic inspired by the behaviour of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by utilizing distributed computing

) is the capacitated minimum spanning tree problem (CMST), which has been effectively tackled by a hybrid ACO algorithm [44] based on a previous ACO algorithm for the CVRP Finally, a class of problems for which ACO has recently shown competitive results is that of multi-level lot-sizing with Assignment and layout

Ant colony optimisation (ACO) could be a comparatively new random heuristic approach for determination optimisation issues. Furthermore, This paper extend these implementations with two local search methods and we compare two heuristics that guide the HACO algorithms. However, relatively few