An ant colony optimization (ACO) approach for the resource-constrained project scheduling problem (RCPSP) is presented. Several new features that are interesting for ACO in general are proposed and evaluated. In particular, the use of a combination of two pheromone evaluation methods by the ants to find new solutions, a change of the influence of the heuristic on the decisions of the ants durin...

In this article we intend to show the use of well-known evolutionary computation techniques Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in an indoor propagation problem. Although these algorithms employ different strategies and computational efforts, they also share certain similarities. Their performance is compared with a genetic algorithm (GA), which is used as refere...

In this paper, we prove polynomial running time bounds for an Ant Colony Optimization (ACO) algorithm for the singledestination shortest path problem on directed acyclic graphs. More specifically, we show that the expected number of iterations required for an ACO-based algorithm with n ants is O( 1 ρ n 2m logn) for graphs with n nodes and m edges, where ρ is an evaporation rate. This result can...

In this paper a new generation ACO-Based Planner, called ACOPlan 2013, is described. This planner is an enhanced version of ACOPlan, a previous ACO-Based Planner (Baioletti et al. 2011), which differs from the former in the search algorithm and in the implementation, now done on top of Downwards. The experimental results, even if are not impressive, are encouraging and confirm that ACO is a sui...

This study considers the routing and wavelength assignment problem (RWA) in optical wavelength-division multiplexed networks. The focus is dynamic traffic, in which the number of wavelengths per fiber is fixed. We attempt to minimize connection blocking using Ant Colony Optimization (ACO). The algorithm employed quantifies the importance of using length and congestion information in making rout...

We describe an Ant Colony Optimization (ACO) algorithm, ANT-MPE, for the most probable explanation problem in Bayesian network inference. After tuning its parameters settings, we compare ANTMPE with four other sampling and local search-based approximate algorithms: Gibbs Sampling, Forward Sampling, Multistart Hillclimbing, and Tabu Search. Experimental results on both artificial and real networ...

Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone trail laying and following behavior of some ant species. Artificial ants in ACO are stochastic solution construction procedures that build candidate solutions for the problem instance under concern by exploiting (artificial) pheromone information that is adapted based on the ants’ search experience and possibly a...

If you want to cite this report, please use the following reference instead: Purpose-In this paper, a novel Ant Colony Optimization (ACO) approach to optimal control is proposed. The standard ACO algorithms have proven to be very powerful optimization metaheuristic for combinatorial optimization problems. They have been demonstrated to work well when applied to various NP-complete problems, suc...

To overcome shortcomings when the ant colony optimization clustering algorithm (ACOC) deal with problem, this paper introduces a novel chaos. The main idea of is to apply chaotic mapping function in two stages optimization: pheromone initialization and update. application phase can encourage ants be distributed as many different initial states possible. Applying update stage add disturbance fac...