Ant Colony Optimization. Marco Dorigo. Thomas Stützle. A Bradford Book. The MIT Press. Cambridge, Massachusetts. London, England. Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algo- . be performed by single ants, such as the invocation of a local optimization. real ant colonies are solving shortest path problems. Ant Colony Optimization takes elements from real ant behavior to solve more complex problems than real .

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ant colony optimization (ACO) meta-heuristic [18], which defines a particular class of ant A laboratory colony of Argentine ants (Iridomyrmex humilis) is given. Ant Colony Optimization (ACO) [31, 32] is a recently proposed metaheuristic ap- The first example of such an algorithm is Ant System (AS) [29, 36, 37, 38]. colony optimization is the foraging behavior of real ant colonies. An example of a Gaussian kernel PDF consisting of five separate Gaussian functions.

Re- tion. It is a prominent illustration of a class of prob- lated variations on the traveling salesman problem include lems in computational complexity theory which are clas- the resource constrained traveling salesman problem which sified as NP-hard. Ant colony optimization ispired by co- has applications in scheduling with an aggregate deadline operative food retrieval have been widely applied unexpect- Pekny and Miller, Most importantly, the traveling edly successful in the combinatorial optimization. This pa- salesman problem often comes up as a subproblem in more per presents an adaptive ant colony optimization algorithm complex combinatorial problems, the best known and im- for traveling salesman problem, which adopts a new selec- portant one of which is the vehicle routing problem, that is, tion mechanism by using held-karp low bound to determine the problem of determining for a fleet of vehicles which cus- the trade-off between the influence of the heuristic informa- tomers should be served by each vehicle and in what order tion and the pheromone trail. The experiments have shown each vehicle should visit the customers assigned to it.

Swarm intelligence Problem solving. Citations Publications citing this paper. Sort by: Influence Recency. Highly Influenced. Efficient optimization methods for freeway management and control Zhe Cong Chennupati Gopinath A local landscape mapping method for protein structure prediction in the HP model Andrea G.

Citrolo , Giancarlo Mauri Natural Computing Aly Thesis , Verfassung der Arbeit In the second example a critical load factor is maximized under a constraint of a maximum of four contiguous plies with the same orientation. Only one material is used graphite-epoxy layer , being the plies orientations the design variables. This contiguity constrain is to prevent matrix cracking [9, 10, 15]. In these two examples, rectangular plates are analyzed and the Classical Lamination Theory see Jones [7] is used to obtain the structural response.

The results of the optimized structures are compared to those obtained from other authors, using GA and ACO. In the third and fourth examples, the ply angles of squares plates with a central hole are optimized, aiming maximizing the fundamental frequency. Plates with different diameters of the central hole are analyzed and the structural response is obtained through a commercial finite element code using Mindlin's plate theory.

The main contribution of this paper is to present an ACO algorithm that has a good performance in different optimization problems of composites lay-up design. Based on the communication of real ants, called stigmergy, the simulation of artificial ants in ACO was developed. When the ants walk from-and-to a food source, they deposit in the ground a chemical substance called pheromone. The quantity of pheromone on the grounds forms a pheromone trails.

Artificial ants may simulate pheromone laying by modifying appropriate pheromone variables associated with problem states they visit while building solutions to the optimization problem Dorigo and Socha [4]. The artificial ants build solutions in stochastic constructive procedures until the connected graph G is complete C, L , where C are the components and the all connections of components C is in the set L.

In a procedure for building a graph, there are two elements associated in this algorithm step. The first one is the pheromone trail , associated with the components C and the connections L.

It influences in the artificial ants' search process, and in the pheromone update by ants. The second is defined by a heuristic value or heuristic information and is related to the problem information. This metaheuristic has a good performance to solve combinatorial optimization problems and has been successfully applied in many complex discrete problems. ACS algorithm has a framework based on three rules that manage the optimization problem.

If it means that the ant is exploiting the learned knowledge based on the pheromone trails and the heuristic information. If the ant explores other tours or search around the best-so-far solution.

The probability distribution is expressed as follows where is a parameter which determines the relative influence of the pheromone trail. The second rule is the Global Pheromone Trail Update, which is given by where is the global pheromone evaporation rate , is the amount of pheromone the ant k deposits on each best-so-far solution and is a set of the best connections. When this rule is applied, both the evaporation and new pheromone deposit are update only to the best-so-far ant.

Local Pheromone Trail Update, the last rule, is applied during the tour construction. The pheromone evaporation and a new pheromone deposit are updated when an ant is exploiting or exploring the connection according to the pseudorandom proportional rule, given by where is the local pheromone evaporation rate , and is the initial pheromone trails value.

In this work the parameters are set following their recommendations. Table 1 shows the values used for each parameter. Merkle and M.

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