Increased Efficiency of the Artificial Bee Colony Algorithm Using the Pheromone Technique

Artificial Bee Colony (ABC) is a powerful metaheuristic algorithm inspired by the behavior of a honey bee swarm. ABC suffers from poor exploitation and, in some cases, poor exploration. Ant Colony Optimization (ACO) is another metaheuristic algorithm that uses pheromones as a guide for an ant to find its way. This study used a pheromone technique from ACO on ABC to enhance its exploration and exploitation. The performance of the proposed method was verified through twenty instances from TSPLIB. The results were compared with the original ABC method and showed that the proposed method leverages the performance of ABC

Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO

An Analysis of Communication Policies for Homogeneous Multi-Colony ACO Algorithms

The increasing availability of parallel hardware encourages the design and adoption of parallel algorithms. In this article, we present a study in which we analyze the impact that different communication policies have on the solution quality reached by a parallel homogeneous multi-colony ACO algorithm for the traveling salesman problem. We empirically test different configurations of each algorithm on a distributed-memory parallel architecture, and analyze the results with a fixed-effects model of the analysis of variance. We consider several factors that influence the performance of a multi-colony ACO algorithm: the number of colonies, migration schedules, communication strategies on different interconnection topologies, and the use of local search. We show that the importance of the communication strategy employed decreases with increasing search effort and stronger local search, and that the relative effectiveness of one communication strategy versus another changes with the addition of local search. © 2010 Elsevier Inc. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

An analysis of communication policies for homogeneous multi-colony ACO algorithms

Université Libre de Bruxelles, Brussels, BelgiumTR/IRIDIA/2009-012info:eu-repo/semantics/publishe