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Ant Colony Optimization (ant + colony_optimization)

Distribution by Scientific Domains

Engineering	22%

Selected Abstracts

An efficient hybrid evolutionary algorithm based on PSO and ACO for distribution feeder reconfiguration

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 5 2010
Taher Niknam
Abstract A new formulation based on norm2 method for the multi objective distribution feeder reconfiguration (DFR) is introduced in order to minimize the real power loss, deviation of the nodes' voltage, the number of switching operations, and to balance the loads on the feeders. In the proposed method, since the objective functions are not the same and commensurable, the objective functions are considered as a vector and the aim is to maximize the distance (norm2) between the objective function vector and the worst objective function vector while the constraints are met. The status of the tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a multi objective and non-differentiable optimization problem so a hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called HPSO, is proposed to solve it. The feasibility of the HPSO algorithm and the proposed DFR is demonstrated and compared with the solutions obtained by other approaches and evolutionary methods such as genetic algorithm (GA), ACO and the original PSO, over different distribution test systems. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Metaheuristics for the vehicle routing problem with loading constraints

NETWORKS: AN INTERNATIONAL JOURNAL, Issue 4 2007
Karl F. Doerner
Abstract We consider a combination of the capacitated vehicle routing problem and a class of additional loading constraints involving a parallel machine scheduling problem. The work is motivated by a real-world transportation problem occurring to a wood-products retailer, which delivers its products to a number of customers in a specific region. We solve the problem by means of two different metaheuristics algorithms: a Tabu Search and an Ant Colony Optimization. Extensive computational results are given for both algorithms, on instances derived from the vehicle routing literature and on real-world instances. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 49(4), 294,307 2007 [source]

Ant colony optimization of clustering models

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 12 2005
Thomas A. Runkler
The original ant system algorithm is simplified leading to a generalized ant colony optimization algorithm that can be used to solve a wide variety of discrete optimization problems. It is shown how objective function based clustering models such as hard and fuzzy c-means can be optimized using particular extensions of this simplified ant optimization algorithm. Experiments with artificial and real datasets show that ant clustering produces better results than alternating optimization because it is less sensitive to local extrema. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1233,1251, 2005. [source]

Ant colony optimization as a method for strategic genotype sampling

ANIMAL GENETICS, Issue 3 2009
M. L. Spangler
Summary A simulation study was carried out to develop an alternative method of selecting animals to be genotyped. Simulated pedigrees included 5000 animals, each assigned genotypes for a bi-allelic single nucleotide polymorphism (SNP) based on assumed allelic frequencies of 0.7/0.3 and 0.5/0.5. In addition to simulated pedigrees, two beef cattle pedigrees, one from field data and the other from a research population, were used to test selected methods using simulated genotypes. The proposed method of ant colony optimization (ACO) was evaluated based on the number of alleles correctly assigned to ungenotyped animals (AKP), the probability of assigning true alleles (AKG) and the probability of correctly assigning genotypes (APTG). The proposed animal selection method of ant colony optimization was compared to selection using the diagonal elements of the inverse of the relationship matrix (A,1). Comparisons of these two methods showed that ACO yielded an increase in AKP ranging from 4.98% to 5.16% and an increase in APTG from 1.6% to 1.8% using simulated pedigrees. Gains in field data and research pedigrees were slightly lower. These results suggest that ACO can provide a better genotyping strategy, when compared to A,1, with different pedigree sizes and structures. [source]

Learning cooperative linguistic fuzzy rules using the best,worst ant system algorithm

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 4 2005
Jorge Casillas
Within the field of linguistic fuzzy modeling with fuzzy rule-based systems, the automatic derivation of the linguistic fuzzy rules from numerical data is an important task. In the last few years, a large number of contributions based on techniques such as neural networks and genetic algorithms have been proposed to face this problem. In this article, we introduce a novel approach to the fuzzy rule learning problem with ant colony optimization (ACO) algorithms. To do so, this learning task is formulated as a combinatorial optimization problem. Our learning process is based on the COR methodology proposed in previous works, which provides a search space that allows us to obtain fuzzy models with a good interpretability,accuracy trade-off. A specific ACO-based algorithm, the Best,Worst Ant System, is used for this purpose due to the good performance shown when solving other optimization problems. We analyze the behavior of the proposed method and compare it to other learning methods and search techniques when solving two real-world applications. The obtained results lead us to remark the good performance of our proposal in terms of interpretability, accuracy, and efficiency. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 433,452, 2005. [source]

High-level synthesis by dynamic ant

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 1-2 2004
Rachaporn Keinprasit
In this article, a new algorithm called dynamic ant is introduced. It was a combination of ant colony optimization (ACO) techniques and the dynamic niche sharing scheme. The interesting point of this algorithm is that it is implemented easily and could be well matched with existing design algorithms by adding the heuristic weights to speed up the algorithm. The algorithm uses the problem state structure as in the reinforcement-learning algorithm, but the storage explosion is prevented by means of the pheromone trail. This algorithm was investigated for the data path design problem of high-level synthesis of which has a large number of design steps and design techniques. © 2004 Wiley Periodicals, Inc. [source]

A software framework for fast prototyping of meta-heuristics hybridization

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 2 2007
Hoong Chuin Lau
Abstract Hybrids of meta-heuristics have been shown to be more effective and adaptable than their parents in solving combinatorial optimization problems. However, hybridized schemes are also more tedious to implement due to their increased complexity. We address this problem by proposing the meta-heuristics development framework (MDF). In addition to being a framework that promotes software reuse to reduce developmental effort, the key strength of MDF lies in its ability to model meta-heuristics using a "request, sense and response" schema, which decomposes algorithms into a set of well-defined modules that can be flexibly assembled through a centralized controller. Under this scheme, hybrid schemes become an event-based search that can adaptively trigger a desired parent's behavior in response to search events. MDF can hence be used to design and implement a wide spectrum of hybrids with varying degrees of collaboration, thereby offering algorithm designers quick turnaround in designing and testing their meta-heuristics. Such technicality is illustrated in the paper through the construction of hybrid schemes using ant colony optimization and tabu search. [source]

Ant colony based hybrid approach for optimal compromise sum-difference patterns synthesis

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 1 2010
P. Rocca
Abstract Dealing with the synthesis of monopulse array antennas, many stochastic optimization algorithms have been used for the solution of the so-called optimal compromise problem between sum and difference patterns when sub-arrayed feed networks are considered. More recently, hybrid approaches, exploiting the convexity of the functional with respect to a sub-set of the unknowns (i.e., the sub-array excitation coefficients) have demonstrated their effectiveness. In this letter, an hybrid approach based on the ant colony optimization (ACO) is proposed. At the first step, the ACO is used to define the sub-array membership of the array elements, while, at the second step, the sub-array weights are computed by solving a convex programming problem. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 128,132, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24882 [source]