PI controller optimization for a heat exchanger through metaheuristic Bat Algorithm, Particle Swarm Optimization, Flower Pollination Algorithm and Cuckoo Search Algorithm

This paper presents the modeling and computer simulation of a control system for a shell and tube heat exchanger, using Bat Algorithms, Particle Swarm Optimization, Flower Pollination Algorithm and Cuckoo Search Algorithm. The temperature control logic is managed by a Proportional Integral-controller whose parameters were initially tuned using two methods of keeping the state of the art. To evaluate the performance of different methods of tuning, we compared the values of the transient of the response to step in eight mesh settings generated. It has also established a comparison between these two types of mesh using the performance indices proposed in the literature, with optimized system by Bat Algorithms got the best values of transient in relation to the Particle Swarm Optimization, Cuckoo Search Algorithm and Flower Pollination Algorithm. Performance indices FPA and PSO obtained better results.


On Strategies to Fix Degenerate k-means Solutions

k-means is a benchmark algorithm used in cluster analysis. It belongs to the large category of heuristics based on location-allocation steps that alternately locate cluster centers and allocate data points to them until no further improvement is possible. Such heuristics are known to suffer from a phenomenon called degeneracy in which some of the clusters are empty. In this paper, we compare and propose a series of strategies to circumvent degenerate solutions during a k-means execution. Our computational experiments show that these strategies are effective, leading to better clustering solutions in the vast majority of the cases in which degeneracy appears in k-means. Moreover, we compare the use of our fixing strategies within k-means against the use of two initialization methods found in the literature. These results demonstrate how useful the proposed strategies can be, specially inside memorybased clustering algorithms.


Otimização de controlador PID para o Self Balancing Segway utilizando Bat Algorithms

Este presente artigo apresenta um estudo inicial do controlador PID sintonizado com o Bat Algorithms. Apresentamos a fundamentação teórica necessária para o aprofundamento do tema proposto neste trabalho. Inicialmente, realizou-se simulações computacionais utilizando Matlab ao controle do sistema dinâmico do SegWay. As simulações computacionais apresentaram resultados promissores. Isso nos motiva, em um outro momento, continuar o estudo com a implementação do SegWay e aplicar a lógica de controle sintonizado pelo Bat Algorithm.