K-means Particle Swarm Optimization Research Articles

Artificial Intelligence enabled Dynamic Bandwidth Allocation Optimization in IoT for improved QoS

The Internet of Things (IoT) is a collection of intelligently connected devices that are connect to the internet. Every day, as technology advances, an IoT device outnumbers humans on the world. The Internet of Things (IoT) systems would connect not only physical users and devices, but also virtual networks and devices that have already been implemented in various scenarios using new networking, computing, and integrated system technologies. With the rapid advancement of technology, the number of devices involved grows rapidly. IoT systems have crucial issues in supplying sufficient bandwidth to IoT Devices for Improving Quality in Services provided in the form of QoS (Quality of Service) parameters, depending on the volume of devices and data. The proposed work is an artificial intelligence enabled dynamic bandwidth allocation optimization in IoT for improved QoS comprising of IoT devices, router, bandwidth data, KM-PSO clustering, Deep Alternative Neural Network (DANN), Dynamic Bandwidth Allocation Optimization, and QoS Estimation. It provides a scalable solution for allocating the bandwidth for the IoT enabled devices for improving QoS parameters. The work uses K-Means-Particle Swarm Optimization (KM-PSO) for generating the optimum number of clusters for improving the accuracy of the proposed work. Once the optimal number of clusters are generated, a Deep Alternative Neural Network (DANN) used in the present invention detects the traffic from the IoT devices, predicts the required bandwidth to be allocated. The Artificial Intelligence enabled Dynamic Bandwidth Allocation Optimization (AIEDBAO) uses Fuzzy Logic Controller (FLC) for allocating Bandwidth based on the traffic rules generated. The improved QoS parameters achieved with the present invention are Bandwidth Consumption of 34.5Mbps, Packet Delivery Delay (PDD) of 32ms, Packet Loss of 4% and Throughput of 99% at supply of 50Mbps

A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm

In the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding similarity. However, this approach fails in managing large-scale datasets. To mitigate the same, an efficient map-reduce-based clustering recommendation system is presented. The proposed method uses a novel variant of the whale optimization algorithm, tournament selection empowered whale optimization algorithm, to attain the optimal clusters. The clustering efficiency of the proposed method is measured on four large-scale datasets in terms of F-measure and computation time. The experimental results are compared with state-of-the-art map-reduce-based clustering methods, namely map-reduce-based K-means, map-reduce-based bat algorithm, map-reduce-based Kmeans particle swarm optimization, map-reduce-based artificial bee colony, and map-reduce-based whale optimization algorithm. Furthermore, the proposed method is tested as a recommendation system on the publicly available movie-lens dataset. The performance validation is measured in terms of mean absolute error, precision and recall, over a different number of clusters. The experimental results assert that the proposed method is a permissive approach for the recommendation over large-scale datasets.

Corner-milling process parameter optimization regarding H62 brass using Kriging model and improved particle swarm optimization algorithm

Single-pass corner milling is common in machining process. Machining quality and cost are the two most important goals for machining process. Machining quality is greatly affected by deformation and mechanical property. Cutting temperature has a very important effect on tool wear, mechanical property, deformation and machining precision. In this paper, cost per volume (CPV) and machining temperature are selected as objectives. To reduce the cost of research, AdvantEdge simulation software was used to simulating the machining process and examined for its accuracy. Comparing the result of AdvantEdge with physical experiments, it showed an average 8% error on CPV and a 6% error on temperature. Given this comparison, simulation data were used to train Kriging model as the surrogate model. Kriging model manifested high accuracy, showing mean error of 6% and 3% in terms of CPV and temperature on validation points. To solve the multi-objective problem, a K-means particle swarm optimization (PSO) was constructed, which outperformed traditional adaptive gird algor (AGA) by finding the better optimal solutions, meanwhile consuming lesser time. Optimization result showed that corner milling should have a low-level spindle speed to ensure low cost and low temperature. High feed rate, cutting depth and cutting width will result in low cost along with high temperature. Optimal parameters should be chosen from Pareto set according to different practical needs. This integrated method manifested its applicability in solving multi-objective problems.

Parallel Hybrid BBO Search Method for Twitter Sentiment Analysis of Large Scale Datasets Using MapReduce

Sentiment analysis is an eminent part of data mining for the investigation of user perception. Twitter is one of the popular social platforms for expressing thoughts in the form of tweets. Nowadays, tweets are widely used for analyzing the sentiments of the users, and utilized for decision making purposes. Though clustering and classification methods are used for the twitter sentiment analysis, meta-heuristic based clustering methods has witnessed better performance due to subjective nature of tweets. However, sequential meta-heuristic based clustering methods are computation intensive for large scale datasets. Therefore, in this paper, a novel MapReduce based K-means biogeography based optimizer(MR-KBBO) is proposed to leverage the strength of biogeography based optimizer with MapReduce model to efficiently cluster the large scale data. The proposed method is validated against four state-of-the-art MapReduce based clustering methods namely; parallel K-means, parallel K-means particle swarm optimization, MapReduce based artificial bee colony optimization, dynamic frequency based parallel k-bat algorithm on four large scale twitter datasets. Further, speedup measure is used to illustrate the computation performance on varying number of nodes. Experimental results demonstrate that the proposed method is efficient in sentiment mining for the large scale twitter datasets.

A Novel Bio-Inspired Algorithm Based on Social Spiders for Improving Performance and Efficiency of Data Clustering

Abstract Since the last decade, the collective intelligent behavior of groups of animals, birds or insects have attracted the attention of researchers. Swarm intelligence is the branch of artificial intelligence that deals with the implementation of intelligent systems by taking inspiration from the collective behavior of social insects and other societies of animals. Many meta-heuristic algorithms based on aggregative conduct of swarms through complex interactions with no supervision have been used to solve complex optimization problems. Data clustering organizes data into groups called clusters, such that each cluster has similar data. It also produces clusters that could be disjoint. Accuracy and efficiency are the important measures in data clustering. Several recent studies describe bio-inspired systems as information processing systems capable of some cognitive ability. However, existing popular bio-inspired algorithms for data clustering ignored good balance between exploration and exploitation for producing better clustering results. In this article, we propose a bio-inspired algorithm, namely social spider optimization (SSO), for clustering that maintains a good balance between exploration and exploitation using female and male spiders, respectively. We compare results of the proposed algorithm SSO with K means and other nature-inspired algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO) and improved bee colony optimization (IBCO). We find it to be more robust as it produces better clustering results. Although SSO solves the problem of getting stuck in the local optimum, it needs to be modified for locating the best solution in the proximity of the generated global solution. Hence, we hybridize SSO with K means, which produces good results in local searches. We compare proposed hybrid algorithms SSO+K means (SSOKC), integrated SSOKC (ISSOKC), and interleaved SSOKC (ILSSOKC) with K means+PSO (KPSO), K means+genetic algorithm (KGA), K means+artificial bee colony (KABC) and interleaved K means+IBCO (IKIBCO) and find better clustering results. We use sum of intra-cluster distances (SICD), average cosine similarity, accuracy and inter-cluster distance to measure and validate the performance and efficiency of the proposed clustering techniques.

Integrating KPSO and C5.0 to analyze the omnichannel solutions for optimizing telecommunication retail

Telecommunication system providers offer many special number commodities, and all marketing staff sell the commodities and special number combinations according to their marketing experience; consequently, telecommunication retailers find it difficult to consider both user demands and profits in their marketing strategies. This study proposes a classification model integrating K-means Particle Swarm Optimization (KPSO) and C5.0. The particles' PSO only followed pbest and gbest when moving, resulting in the disadvantage that PSO may easily fall into a local optimal solution. First, clustering analysis is carried out using the KPSO clustering method. Second, classification rules for clustering results are formulated by the C5.0 classification method, and a classification model is established in order to achieve effective descriptions of the clustering rules. The methods proposed herein can help retailers find and utilize complementary tariff products for mobile numbers as the basis for future sales and procurement. This study also analyzes the best media for customers' mobile phone purchase methods and utilizes different groups of buyers of the omnichannel. The proposed model is also able to categorize new future tariffs and can further conceptualize the clustering results in the analysis of telecom tariffs and product mix. Finally, the results effectively assist the telecommunications retail industry when considering procurement, product projects, sales, and marketing solutions.

Energy optimization in wireless sensor networks using a hybrid K-means PSO clustering algorithm

Energy saving in wireless sensor networks (WSNs) is a critical problem for diversity of applications. Data aggregation between sensor nodes is huge unless a suitable sensor data ow management is adopted. Clustering the sensor nodes is considered an effective solution to this problem. Each cluster should have a controller denoted as a cluster head (CH) and a number of nodes located within its supervision area. Clustering demonstrated an effective result in forming the network into a linked hierarchy. Thus, balancing the load distribution in WSNs to make efficient use of the available energy sources and reducing the traffic transmission can be achieved. In solving this problem we need to �nd the optimal distribution of sensors and CHs; thus, we can increase the network lifetime while minimizing the energy consumption. In this paper, we propose our initial idea on providing a hybrid clustering algorithm based on K-means clustering and particle swarm optimization (PSO); named KPSO to achieve efficient energy management of WSNs. Our KPSO algorithm is compared with traditional clustering techniques such as the low energy adaptive clustering hierarchy (LEACH) protocol and K-means clustering separately.

IJARCCE - Computer and Communication Engineering

1 Abstract: Data clustering is a popular approach for automatically finding set of objects into a specific number of clusters. Clustering is largely used in many fields including text mining, information retrieval and pattern grouping. Particle Swarm Optimization (PSO) is a population-based optimization algorithm modelled after the simulation of social behaviour of bird flocks and widely used for optimize problem solving. In clustering problem PSO gives optimal solution but takes long time (so called iterations) to find the optimum solution. The hybrid PSO and K-means algorithm is developed to automatically detect the cluster centers of geometrical structure data sets. The proposed algorithm gives the benefits for each of two-merged algorithms. K-means is fast algorithm, PSO optimize the solution. The implementation of the hybrid K-means PSO structure is realized in hardware. The clustering based on hybrid K-means PSO architecture is described by different technique for hardware description (i.e. block diagram) and implemented on field programmable gate array (FPGA). Its feasibility is verified by experiments. Results show that the proposed architecture implemented on the FPGA has a good clustering technique especially for testing with color reduction for true color video.

K-means particle swarm optimization with embedded chaotic search for solving multidimensional problems

The proposed approach inherited the paradigm in particle swarm optimization (PSO) to implement a chaotic search around global best position (gbest) and enhanced by K-means clustering algorithm, named KCPSO. K-means with clustering property in PSO resulted in rapid convergence while chaotic search with ergodicity characteristic in PSO contributed to refine gbest. Experimental results indicated that the proposed KCPSO approach could evidently speed up convergence and successfully solving complex multidimensional problems. Besides, KCPSO was compared with canonical PSO in performance. And, a case study was also employed to demonstrate the validity of the proposed approach.