Summary The collective and self-organization behaviors of social insects have inspired researchers to reproduce them. Methods inspired by ants are a great promise for clustering problems. In the clustering algorithm based on Ants, patterns are spread out on a grid and each ant has assigned a pattern. The ants are responsible for picking, transporting and dropping patterns on the grid. After the algorithm converges, the recovery of clusters is done using the patterns’ positions on the grid. The purpose with this paper is to present changes and improvements to the Ant-based Clustering algorithm originally proposed by [2], hereinafter called proposed algorithm, evaluating its performance relatively to the Ward Method, the Kohonen Maps and the ACAM algorithm (Antbased Clustering Algorithm Modified), proposed by [1]. The major changes in the proposed algorithm were: the introduction of a comparison between the probability of dropping a pattern at the position chosen randomly and the probability of dropping this pattern at its current position; the introduction of an evaluation of the probability of a neighboring position when the decision to drop a pattern is positive and the cell in which the pattern should be dropped is occupied; and the substitution of the pattern carried by an ant in case this pattern is not dropped within 100 consecutive iterations. To assess the performance of the proposed algorithm three real and public databases were used (IRIS, WINE and PIMA Indians Diabetes). The results showed superior performance of the proposed algorithm over the ACAM for two of the three databases and equality with other methods.
Schedule Generation and Fleet Assignment problems usually are solved separately. The integrated solution for both problems, although desirable, leads to large scale models of the NP-Hard class. This article presents a mathematical formulation of this integrated problem along with a new heuristical approach, called MAGS, based on the ACO metaheuristic. Both the exact solution and the one provided by MAGS are obtained and compared for the case of a Brazilian airline. The results have shown the applicability of MAGS to real world cases.
The Hough transform is a powerful tool in image analysis, e.g. circle detection is a fundamental issue in image processing applications of industrial parts or tools. Because of its drawbacks, various modifications of the basic circle Hough transform (CHT) method have been suggested. This paper presents a modified method based on the basic CHT algorithm and using no trigonometric calculations in order to improve the computational performance of the voting process for a good accuracy and robustness of circle detection in a binary image. This paper also provides the errors analysis of the proposed method against the basic CHT method to illustrate that it can replace the basic CHT method for small values of the resolution e of the angle θ. It then compete the CORDIC algorithm when implemented into digital device.
In the Particle Swarm Optimization (PSO) algorithm, the expense of evaluating the objective function can make it difficult, or impossible, to use this approach effectively; reducing the number of necessary function evaluations would make it possible to apply the PSO algorithm more widely. Many function approximation techniques have been developed that address this issue, but an alternative to function approximation is function conservation. We describe GREEN-PSO (GR-PSO), an algorithm that, given a fixed number of function evaluations, conserves those function evaluations by probabilistically choosing a subset of particles smaller than the entire swarm on each iteration and allowing only those particles to perform function evaluations. The “surplus” of function evaluations thus created allows a greater number of particles and/or iterations. In spite of the loss of information resulting from this more parsimonious use of function evaluations, GR-PSO performs as well as, or better than, the standard PSO algorithm on a set of six benchmark functions, both in terms of the rate of error reduction and the quality of the final solution.
Multi-modal optimisation problems are characterised by the presence of either local sub-optimal points or a number of equally optimal points. These local optima can be considered as point attractors for hill climbing search algorithms. It is desirable to be able to model them either to avoid mistaking a local optimum for a global one or to allow the discovery of multiple equally optimal solutions. Hopfield neural networks are capable of modelling a number of patterns as point attractors which are learned from known patterns. This paper shows how a Hopfield network can model a number of point attractors based on non-optimal samples from an objective function. The resulting network is shown to be able to model and generate a number of local optimal solutions up to a certain capacity. This capacity, and a method for extending it is studied.
