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In recent years, the development of multi-classifier systems has become an active research field. A multi-classifier system is an ensemble of classification algorithms whose individual outputs are fused together for better accuracy and interpretability. An important aspect when designing such systems is related to the heterogeneity of the building blocks (classifiers) that make up the ensemble, since previous studies have uncovered that a more diverse ensemble often boosts up the overall classification power. Some statistical measures can be used to estimate how diverse the classifier ensembles are; they are called diversity measures. Another issue to be considered is the number of individual classifiers included in the model: the lower the number of classifiers, the simpler the resulting system. In general terms, the parsimony principle is highly desired in such ensembles, since a bulky ensemble will also be a very time-consuming model. Finding the minimal subset of individual classifiers that brings about the best system performance can be posed as a combinatorial optimization problem. In this paper, we address the problem of building multi-classifiers systems from the perspective of Ant Colony Optimization (ACO), a widely popular and effective metaheuristic optimization algorithm. The main reason behind the use of ACO lies on its strong ability to solve entangled combinatorial optimization problems. An empirical analysis is included to statistically validate the benefits of our proposal. [ABSTRACT FROM AUTHOR]

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Heat transfer processes are one of the most important problems to be solved in the field of Engineering. Among the most widely used equipment for heat transfer in the industry are the shell and tube heat exchangers. This paper develops the procedure for optimizing the design of shell and tube heat exchangers using the method of Kern and applying the ant colony algorithm. The procedure has been applied to three specific examples and the results obtained are compared with those obtained by applying other methods of artificial intelligence. The main geometric parameters of shell and tube heat exchangers are optimized, to achieve a lower cost of the exchanger. The efficacy of the new procedure MACO (Mixed Ant Colony Optimization) for the optimization process from economically point of view was demonstrated, using different case studies. [ABSTRACT FROM AUTHOR]