No abstract

No abstract

Particle Swarm Optimisers are inherently distributed algorithms where the solution for a problem emerges from the interactions between many simple individual agents called particles. This article proposes the use of the Particle Swarm Optimiser as a new tool for Data Mining. In the first phase of our research, three different Particle Swarm Data Mining Algorithms were implemented and tested against a Genetic Algorithm and a Tree Induction Algorithm (J48). From the obtained results, Particle Swarm Optimisers proved to be a suitable candidate for classification tasks. The second phase was dedicated to improving one of the Particle Swarm optimiser variants in terms of attribute type support and temporal complexity. The data sources here used for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that Particle Swarm Data Mining Algorithms are competitive, not only with other evolutionary techniques, but also with industry standard algorithms such as the J48 algorithm, and can be successfully applied to more demanding problem domains.

The experiments carried out in this investigation were oriented in order to optimize the properties of cork-based agglomerates as an ideal core material for sandwich components of lightweight structures, such as those used in aerospace applications. Static bending tests were performed in order to characterize the mechanical strength of different types of cork agglomerates which were obtained considering distinct production variables. The ability to withstand dynamic loads was also evaluated from a set of impact tests using carbon-cork sandwich specimens. The results got from experimental tests revealed that cork agglomerates performance essentially depends on the cork granule size, its density and the bonding procedure used for the cohesion of granulates, and these parameters can be adjusted in function of the final application intended for the sandwich component. These results also allow inferring that optimized cork agglomerates have some specific properties that confirm their superior ability as a core material of sandwich components when compared with other conventional materials.

Abstract. In this paper we present and discuss the results of experimentally comparing the performance of several variants of the standard swarm particle optimiser and a new approach to swarm based optimisation. The new algorithm, which we call predator prey optimiser, combines the ideas of particle swarm optimisation with a predator prey inspired strategy, which is used to maintain diversity in the swarm and preventing premature convergence to local suboptima. This algorithm and the most common variants of the particle swarm optimisers are tested in a set of multimodal functions commonly used as benchmark optimisation problems in evolutionary computation.

No abstract

No abstract

No abstract