Adaptive firefly algorithm for hierarchical text clustering

Text clustering is essentially used by search engines to increase the recall and precision in information retrieval. As search engine operates on Internet content that is constantly being updated, there is a need for a clustering algorithm that offers automatic grouping of items without prior knowledge on the collection. Existing clustering methods have problems in determining optimal number of clusters and producing compact clusters. In this research, an adaptive hierarchical text clustering algorithm is proposed based on Firefly Algorithm. The proposed Adaptive Firefly Algorithm (AFA) consists of three components: document clustering, cluster refining, and cluster merging. The first component introduces Weight-based Firefly Algorithm (WFA) that automatically identifies initial centers and their clusters for any given text collection. In order to refine the obtained clusters, a second algorithm, termed as Weight-based Firefly Algorithm with Relocate (WFAR), is proposed. Such an approach allows the relocation of a pre-assigned document into a newly created cluster. The third component, Weight-based Firefly Algorithm with Relocate and Merging (WFARM), aims to reduce the number of produced clusters by merging nonpure clusters into the pure ones. Experiments were conducted to compare the proposed algorithms against seven existing methods. The percentage of success in obtaining optimal number of clusters by AFA is 100% with purity and f-measure of 83% higher than the benchmarked methods. As for entropy measure, the AFA produced the lowest value (0.78) when compared to existing methods. The result indicates that Adaptive Firefly Algorithm can produce compact clusters. This research contributes to the text mining domain as hierarchical text clustering facilitates the indexing of documents and information retrieval processes

Linearized biogeography-based optimization with re-initialization and local search

Biogeography-based optimization (BBO) is an evolutionary optimization algorithm that uses migration to share information among candidate solutions. One limitation of BBO is that it changes only one independent variable at a time in each candidate solution. In this paper, a linearized version of BBO, called LBBO, is proposed to reduce rotational variance. The proposed method is combined with periodic re-initialization and local search operators to obtain an algorithm for global optimization in a continuous search space. Experiments have been conducted on 45 benchmarks from the 2005 and 2011 Congress on Evolutionary Computation, and LBBO performance is compared with the results published in those conferences. The results show that LBBO provides competitive performance with state-of-the-art evolutionary algorithms. In particular, LBBO performs particularly well for certain types of multimodal problems, including high-dimensional real-world problems. Also, LBBO is insensitive to whether or not the solution lies on the search domain boundary, in a wide or narrow basin, and within or outside the initialization domain

Potassium fertilization in bareroot nurseries in the southern US: a review

This review covers most of the published literature on potassium (K) fertilization in bareroot seedbeds with the intent to concentrate on the southern United States. The timing and rates of K fertilization for bareroot seedlings are often based on logic and myths and, as a result, K recommendations vary considerably. Some recommend bareroot pine seedlings be fertilized with twice as much K as nitrogen (N) while others apply less than 100 kg ha-1. It was determined that several long-held claims about K fertilization are invalid. Nursery seedbeds do not need to contain four times as much available K as N and the belief that extra K fertilization will increase freeze tolerance or drought resistance of non-deficient seedlings is invalid. There are no data to support the claim that K fertilization increases root growth or assists in the formation of terminal buds. For sandy seedbeds, there is no need to apply K before sowing. Adding extra K during the fall does not increase seedling morphology or seedling performance when loblolly pine seedlings, at lifting, have more than 0.5% K in needles. A reduction of K fertilization can be achieved by reviewing foliar tests prior to K top-dressings

Geotechnical Technique To Strengthen Traditional I-Walls Through The Use Of Composite Material

On Monday, August 29, 2005, Hurricane Katrina struck the southern gulf coast of the United States. Hurricane Katrina produced record high amounts of surge water along the coastal regions, that over topped and eroded levees and flood walls, inevitably flooding the entire city of New Orleans. After Hurricane Katrina, the Department of Homeland Security approved a research grant to The University of Mississippi to develop effective but economic technique of strengthening existing I-walls. Using composite materials, a new retrofitting technique was developed to increase the stability of existing I-walls by implementing composite caps. For economical and practical testing, scale models were tested for validation of results. Anisotropic scaling of materials dictated that each test become an independent case study. Each composite cap contains a principle fiber characteristic making each unique. The caps were denoted with a number to signify difference in mechanical properties. The cap denoted as Cap1 had a fiber orientation with a principle direction of X, Cap2 with a principle direction of Y, and Cap3 with a principle direction of X, with the difference being double the fibers and the addition of filler for Cap3. Therefore this study can state which cap exhibited superior structural integrity while sustaining high loading conditions and ultimately lowered the relative displacement of adjacent I-walls. The major findings were that the fiber orientation, thickness, and length, coupled with the application method of the material, played a major role in the structural integrity of the cap. Observations during testing shothat there was an effective height of the cap. When choosing the optimum geometrical properties of composite caps, one must consider the integrity of the concrete. If a cap is too small and extremely stiff, the cap can potentially damage the concrete due to tension. If the cap is too large and thin, then it is economically not viable and may continue to produce high relative displacement. Through intensive testing, the University of Mississippi\u27s Retrofitted Composite Cap System (UMRCCS) has been proven as a viable geotechnical technique for economically and structurally improving I-wall structures, susceptible to highly deviated soil types that are prone to catastrophic failure

Annual Report of Research and Creative Productions by Faculty and Staff, January to December, 2019

Annual Report of Research and Creative Productions by Faculty and Staff, January to December, 2019

A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm

We compare 27 modifications of the original particle swarm optimization (PSO) algorithm. The analysis evaluated nine basic PSO types, which differ according to the swarm evolution as controlled by various inertia weights and constriction factor. Each of the basic PSO modifications was analyzed using three different distributed strategies. In the first strategy, the entire swarm population is considered as one unit (OC-PSO), the second strategy periodically partitions the population into equally large complexes according to the particle’s functional value (SCE-PSO), and the final strategy periodically splits the swarm population into complexes using random permutation (SCERand-PSO). All variants are tested using 11 benchmark functions that were prepared for the special session on real-parameter optimization of CEC 2005. It was found that the best modification of the PSO algorithm is a variant with adaptive inertia weight. The best distribution strategy is SCE-PSO, which gives better results than do OC-PSO and SCERand-PSO for seven functions. The sphere function showed no significant difference between SCE-PSO and SCERand-PSO. It follows that a shuffling mechanism improves the optimization process

Soil and forest productivity: a case study from Stone pine (Pinus pinea L.) stands in Calabria (southern Italy)

The objective of this study was to determine whether edaphic and/or topographic variables may be used as predictors of site productivity in Stone pine stands in Calabria (southern Italy). To accomplish this goal, a linear discriminant rule was developed using data from 16 pure Stone pine stands, grouped into three different classes based on the mean dominant height annual growth. The discriminant rule was based on three linear models (one for each class) that jointly predicts site class for a given stand. To test the accuracy of the proposed method, cross-validation was carried out by developing 16 alternative discriminant rules (excluding the analyzed data). Predictors tested were edaphic (texture, pH, organic matter) and topographic (altitude and slope) variables. The model obtained allow to discriminate poorest sites accurately (100% of sites were correctly re-classified using the discriminant functions obtained). In more productive areas, sites were correctly re-classified in the 33.33% of cases, while in intermediate sites the correct classification was equal to 50%. Our discriminant rule classifies correctly the poorest stands, suggesting that site index in plain site soils strongly depends on clay percentage. Overall, the edaphic model obtained classifies plots into the correct site index class 61.11% of cases, which is considered an acceptable value for these kinds of studies.Instituto Universitario de Gestión Forestal Sostenibl