Variable Selection and Fault Detection Using a Hybrid Intelligent Water Drop Algorithm
01 Jan 2014-pp 225-231
TL;DR: A recently proposed swarm intelligence-based hybrid intelligent water drop (IWD) optimization algorithm in combination with support vector machines and an information gain heuristic for selecting a subset of relevant fault indicators demonstrates its viability as a strong candidate for complex classification and prediction tasks.
Abstract: Process fault detection concerns itself with monitoring process variables and identifying when a fault has occurred in the process workflow. Sophisticated learning algorithms may be used to select the relevant process state variables out of a massive search space and can be used to build more efficient and robust fault detection models. In this study, we present a recently proposed swarm intelligence-based hybrid intelligent water drop (IWD) optimization algorithm in combination with support vector machines and an information gain heuristic for selecting a subset of relevant fault indicators. In the process, we demonstrate the successful application and effectiveness of this swarm intelligence-based method to variable selection and fault identification. Moreover, performance testing on standard machine learning benchmark datasets also indicates its viability as a strong candidate for complex classification and prediction tasks.
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TL;DR: Results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy and is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method.
Abstract: The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems.
13 citations
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TL;DR: A novel fault detection and mitigation approach that not only reduces the resources wastage but ensures timely delivery of services to avoid any penalty due to service level agreement (SLA) violation is proposed.
Abstract: Distributed Systems have swiftly evolved from network of personal computers to cluster and then to grid, moving on to the era of cloud computing and now the latest one as Internet of things (IoT). With these rapid enhancements, the scale and complexity of systems providing cloud computing services have also increased tremendously. The major challenge faced by cloud service providers today is to provide an efficient, cost-effective, and reliable solution for seamless delivery of services to users. To achieve this research community is constantly working hard on different related issues like scheduling, power consumption, high availability, customer retention, resource provisioning, reliability and minimizing the probability of failures, etc. Reliability of service is an important parameter. With a large number of components in the cloud, the probability of failures is becoming a norm rather than an exception while delivering services to users. This emphasizes the need to develop fault tolerant schemes for cloud environment to deliver the required level of reliability. In this work, we have proposed a novel fault detection and mitigation approach. The novelty of approach lies in the method of detecting the fault based on running status of the job. The detection algorithm periodically monitors the progress of job on virtual machines (VMs) and reports the stalled job due to failed VM to fault tolerant manager (FTM). This not only reduces the resources wastage but ensures timely delivery of services to avoid any penalty due to service level agreement (SLA) violation. The validation of the proposed approach is done using CloudSim simulator. The performance analysis reveals the effectiveness of the proposed approach.
13 citations
Cites methods from "Variable Selection and Fault Detect..."
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TL;DR: An emerging evolutionary and swarm-based intelligent water drops algorithm for email spam classification along with the machine learning classification technique known as naive Bayes classifier is proposed.
Abstract: The paper proposes an emerging evolutionary and swarm-based intelligent water drops algorithm for email spam classification The proposed algorithm is used along with the machine learning classification technique known as naive Bayes classifier The intelligent water drops algorithm is used for feature subset construction, and naive Bayes classifier is applied over the subset to classify the email as spam or not spam The result of the hybrid method is compared with other evolutionary algorithm used with machine learning classifiers The proposed algorithm outperforms the other hybrid algorithms
10 citations
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TL;DR: A method for feature subset selection using cross-validation that is applicable to any induction algorithm is described, and experiments conducted with ID3 and C4.5 on artificial and real datasets are discussed.
Abstract: We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets.
2,477 citations
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TL;DR: In this article, a model of an industrial chemical process for the purpose of developing, studying and evaluating process control technology is presented, which is well suited for a wide variety of studies including both plantwide control and multivariable control problems.
Abstract: This paper describes a model of an industrial chemical process for the purpose of developing, studying and evaluating process control technology. This process is well suited for a wide variety of studies including both plant-wide control and multivariable control problems. It consists of a reactor/ separator/recycle arrangement involving two simultaneous gas—liquid exothermic reactions of the following form: A(g) + C(g) + D(g) → G(liq), Product 1, A(g) + C(g) + E(g) → H(liq), Product 2. Two additional byproduct reactions also occur. The process has 12 valves available for manipulation and 41 measurements available for monitoring or control. The process equipment, operating objectives, process control objectives and process disturbances are described. A set of FORTRAN subroutines which simulate the process are available upon request. The chemical process model presented here is a challenging problem for a wide variety of process control technology studies. Even though this process has only a few unit operations, it is much more complex than it appears on first examination. We hope that this problem will be useful in the development of the process control field. We are also interested in hearing about applications of the problem.
2,216 citations
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TL;DR: In this paper, the development and performance of four plantwide control structures for the Tennessee Eastman challenge problem is described. But the control structures are developed in a tiered fashion and without the use of a quantitative steady state or dynamic model of the process.
Abstract: This study focuses on the development and performance of four plant-wide control structures for the Tennessee Eastman challenge problem The control structures are developed in a tiered fashion and without the use of a quantitative steady state or dynamic model of the process The throughput or production rate manipulator is selected first so that it is located on the major process path The inventory controls are arranged in an outward direction from this throughput manipulator The four structures are described and comments are given on their effective handling of the defined disturbances and setpoint changes One structure provides effective control under all circumstances for 50 hours of process time The effective dynamic performance of these structures supports the strength of the tiered plant-wide control design methodology used
436 citations
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TL;DR: The intelligent water drops (IWD) algorithm is tested to find solutions of the n-queen puzzle with a simple local heuristic and the travelling salesman problem (TSP) is also solved with a modified IWD algorithm.
Abstract: A natural river often finds good paths among lots of possible paths in its ways from the source to destination. These near optimal or optimal paths are obtained by the actions and reactions that occur among the water drops and the water drops with the riverbeds. The intelligent water drops (IWD) algorithm is a new swarm-based optimisation algorithm inspired from observing natural water drops that flow in rivers. In this paper, the IWD algorithm is tested to find solutions of the n-queen puzzle with a simple local heuristic. The travelling salesman problem (TSP) is also solved with a modified IWD algorithm. Moreover, the IWD algorithm is tested with some more multiple knapsack problems (MKP) in which near-optimal or optimal solutions are obtained.
375 citations
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