Spatial quality evaluation for drinking water based on GIS and ant colony clustering algorithm

To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.

Incremental Web Usage Mining Based on Active Ant Colony Clustering

To alleviate the scalability problem caused by the increasing Web using and changing users＇ interests, this paper presents a novel Web Usage Mining algorithm-Incremental Web Usage Mining algorithm based on Active Ant Colony Clustering. Firstly, an active movement strategy about direction selection and speed, different with the positive strategy employed by other Ant Colony Clustering algorithms, is proposed to construct an Active Ant Colony Clustering algorithm, which avoid the idle and ＂flying over the plane＂ moving phenomenon, effectively improve the quality and speed of clustering on large dataset. Then a mechanism of decomposing clusters based on above methods is introduced to form new clusters when users＇ interests change. Empirical studies on a real Web dataset show the active ant colony clustering algorithm has better performance than the previous algorithms, and the incremental approach based on the proposed mechanism can efficiently implement incremental Web usage mining.

Text clustering based on fusion of ant colony and genetic algorithms

Focusing on the problem that the ant colony algorithm gets into stagnation easily and cannot fully search in solution space,a text clustering approach based on the fusion of the ant colony and genetic algorithms is proposed.The four parameters that influence the performance of the ant colony algorithm are encoded as chromosomes,thereby the fitness function,selection,crossover and mutation operator are designed to find the combination of optimal parameters through a number of iteration,and then it is applied to text clustering.The simulation results show that compared with the classical k-means clustering and the basic ant colony clustering algorithm,the proposed algorithm has better performance and the value of F-Measure is enhanced by 5.69%,48.60%and 69.60%,respectively,in 3 test datasets.Therefore,it is more suitable for processing a larger dataset.

Storage Assignment Optimization in a Multi-tier Shuttle Warehousing System

The current mathematical models for the storage assignment problem are generally established based on the traveling salesman problem（TSP）,which has been widely applied in the conventional automated storage and retrieval system（AS/RS）.However,the previous mathematical models in conventional AS/RS do not match multi-tier shuttle warehousing systems（MSWS） because the characteristics of parallel retrieval in multiple tiers and progressive vertical movement destroy the foundation of TSP.In this study,a two-stage open queuing network model in which shuttles and a lift are regarded as servers at different stages is proposed to analyze system performance in the terms of shuttle waiting period（SWP） and lift idle period（LIP） during transaction cycle time.A mean arrival time difference matrix for pairwise stock keeping units（SKUs） is presented to determine the mean waiting time and queue length to optimize the storage assignment problem on the basis of SKU correlation.The decomposition method is applied to analyze the interactions among outbound task time,SWP,and LIP.The ant colony clustering algorithm is designed to determine storage partitions using clustering items.In addition,goods are assigned for storage according to the rearranging permutation and the combination of storage partitions in a 2D plane.This combination is derived based on the analysis results of the queuing network model and on three basic principles.The storage assignment method and its entire optimization algorithm method as applied in a MSWS are verified through a practical engineering project conducted in the tobacco industry.The applying results show that the total SWP and LIP can be reduced effectively to improve the utilization rates of all devices and to increase the throughput of the distribution center.

A new algorithm based on metaheuristics for data clustering

This paper presents a new algorithm for clustering a large amount of data.We improved the ant colony clustering algorithm that uses an ant’s swarm intelligence,and tried to overcome the weakness of the classical cluster analysis methods.In our proposed algorithm,improvements in the efficiency of an agent operation were achieved,and a new function "cluster condensation" was added.Our proposed algorithm is a processing method by which a cluster size is reduced by uniting similar objects and incorporating them into the cluster condensation.Compared with classical cluster analysis methods,the number of steps required to complete the clustering can be suppressed to 1% or less by performing this procedure,and the dispersion of the result can also be reduced.Moreover,our clustering algorithm has the advantage of being possible even in a small-field cluster condensation.In addition,the number of objects that exist in the field decreases because the cluster condenses;therefore,it becomes possible to add an object to a space that has become empty.In other words,first,the majority of data is put on standby.They are then clustered,gradually adding parts of the standby data to the clustering data.The method can be adopted for a large amount of data.Numerical experiments confirmed that our proposed algorithm can theoretically applied to an unrestricted volume of data.