Metaheuristics in large-scale global continues optimization

Identification of variable interaction is essential for an efficient implementation of a divide-and-conquer algorithm for large-scale black-box optimization. In this paper, we propose an improved variant of the differential grouping (DG) algorithm, which has a better efficiency and grouping accuracy. The proposed algorithm, DG2, finds a reliable threshold value by estimating the magnitude of roundoff errors. With respect to efficiency, DG2 reuses the sample points that are generated for detecting interactions and saves up to half of the computational resources on fully separable functions. We mathematically show that the new sampling technique achieves the lower bound with respect to the number of function evaluations. Unlike its predecessor, DG2 checks all possible pairs of variables for interactions and has the capacity to identify overlapping components of an objective function. On the accuracy aspect, DG2 outperforms the state-of-the-art decomposition methods on the latest large-scale continuous optimization benchmark suites. DG2 also performs reliably in the presence of imbalance among contribution of components in an objective function. Another major advantage of DG2 is the automatic calculation of its threshold parameter ( $\epsilon $ ), which makes it parameter-free. Finally, the experimental results show that when DG2 is used within a cooperative co-evolutionary framework, it can generate competitive results as compared to several state-of-the-art algorithms.

/pdf/dg2-a-faster-and-more-accurate-differential-grouping-for-2q8dmeaqxc.pdf

DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization

Fault diagnosis of chemical process data becomes one of the most important directions in research and practice. Conventional fault diagnosis and classification methods first extract features from the raw process data. Then certain classifiers are adopted to make diagnosis. However, these conventional methods suffer from the expertise of feature extraction and classifier design. They also lack the adaptive processing of the dynamic information in raw data. This paper proposes a fault diagnosis method based on long short-term memory (LSTM) neural network. The novel method can directly classify the raw process data without specific feature extraction and classifier design. It is also able to adaptively learn the dynamic information in raw data. First, raw process data are used to train the LSTM neural network until the cost function of LSTM converges below certain predefined small positive value. In this step, the dynamic information of raw process data is adaptively learned by LSTM. Then testing data are used to obtain the diagnosis results of the trained LSTM neural network. The application of LSTM to fault identification and analysis is evaluated in the Tennessee Eastman benchmark process. Extensive experimental results show LSTM can better separate different faults and provide more promising fault diagnosis performance.

Sequential Fault Diagnosis Based on LSTM Neural Network

Population size of Differential Evolution (DE) algorithms is often specified by user and remains fixed during run. During the first decade since the introduction of DE the opinion that its population size should be related to the problem dimensionality prevailed, later the approaches to DE population size setting diversified. In large number of recently introduced DE algorithms the population size is considered to be problem-independent and often fixed to 100 or 50 individuals, but alongside a number of DE variants with flexible population size have been proposed. The present paper briefly reviews the opinions regarding DE population size setting and verifies the impact of the population size on the performance of DE algorithms. Ten DE algorithms with fixed population size, each with at least five different population size settings, and four DE algorithms with flexible population size are tested on CEC2005 benchmarks and CEC2011 real-world problems. It is found that the inappropriate choice of the population size may severely hamper the performance of each DE algorithm. Although the best choice of the population size depends on the specific algorithm, number of allowed function calls and problem to be solved, some rough guidelines may be sketched. When the maximum number of function calls is set to classical values, i.e. those specified for CEC2005 and CEC2011 competitions, for low-dimensional problems (with dimensionality below 30) the population size equal to 100 individuals is suggested; population sizes smaller than 50 are rarely advised. For higher-dimensional artificial problems the population size should often depend on the problem dimensionality d and be set to 3d–5d. Unfortunately, setting proper population size for higher-dimensional real-world problems (d>40) turns out too problem and algorithm-dependent to give any general guide; 200 individuals may be a first guess, but many DE approaches would need a much different choice, ranging from 50 to 10d. However, quite clear relation between the population size and the convergence speed has been found, showing that the fewer function calls are available, the lower population sizes perform better. Based on the extensive experimental results the use of adaptive population size is highly recommended, especially for higher-dimensional and real-world problems. However, which specific algorithms with population size adaptation perform better depends on the number of function calls allowed.

Review of Differential Evolution population size

Leakage detection and localization in pipelines has become an important aspect of water management systems Since monitoring leakage in large-scale water distribution networks (WDNs) is a challenging task, the need to develop a reliable and robust leak detection and localization technique is essential for loss reduction in potable WDNs In this paper, some of the existing techniques for water leakage detection are discussed and open research areas and challenges are highlighted It is concluded that despite the numerous research efforts and advancement in leakage detection technologies, a large scope is still open for further research in this domain One such area is the effective detection of background type leakages that have not been covered fully in the literature The utilization of wireless sensor networks for leakage detection purposes, its technical challenges as well as some future research areas are also presented In a general remark, practical application of these techniques for large-scale water distribution networks is still a major concern In this paper, an overview of this important problem is addressed

Towards Achieving a Reliable Leakage Detection and Localization Algorithm for Application in Water Piping Networks: An Overview

Negative pressure wave is a popular method to detect the occurrence and location of leak incidents in oil/gas pipeline. Three core technical challenges and related algorithm are discussed in this paper. The first is data quality. The balance between noise level and locating precision is discussed in filter design. The second one is dynamic slope in anomaly detection, whence a bi-SPC (Static Process Control) algorithms is proposed to make the threshold be adaptive. The third one is the false alarm caused normal working condition changes. Multiple-sensor paring algorithms is presented. With these algorithms, the robustness and locating precision of LDS (Leak Detection System) can be ensured.

Negative pressure wave based pipeline Leak Detection: Challenges and algorithms

Water pipe failures can not only have a great impact on people's daily life but also cause significant waste of water which is an essential and precious resource to human beings. As a result, preventative maintenance for water pipes, particularly in urban-scale networks, is of great importance for a sustainable society. To achieve effective replacement and rehabilitation, failure prediction aims to proactively find those 'most-likely-to-fail' pipes becomes vital and has been attracting more attention from both academia and industry, especially from the civil engineering field. This paper presents an already-deployed industrial computational system for pipe failure prediction. As an alternative to risk matrix methods often depending on ad-hoc domain heuristics, learning based methods are adopted using the attributes with respect to physical, environmental, operational conditions and etc. Further challenge arises in practice when lacking of profile attributes. A dive into the failure records shows that the failure event sequences typically exhibit temporal clustering patterns, which motivates us to use the stochastic process to tackle the failure prediction task. Specifically, the failure sequence is formulated as a self-exciting stochastic process which is, to our best knowledge, a novel formulation for pipe failure prediction. And we show that it outperforms a baseline assuming the failure risk grows linearly with aging. Broad new problems and research points for the machine learning community are also introduced for future work.

Towards effective prioritizing water pipe replacement and rehabilitation

This paper proposes a novel gender recognition method based on the head-shoulder part of human body. The head-shoulder area contains much information that could be cues to infer the gender of a person, such as hair-style, face, neckline style and so on. A rich high-dimensional feature descriptor is designed to extract gradient, texture and orientation information from the head-shoulder area, then Partial Least Squares (PLS) is employed to learn a very low dimensional discriminative subspace. Features are projected into the low dimensional subspace and linear SVM is employed to learn an efficient classification model between the male and female categories. Experimental results on a large real-world dataset demonstrate the effectiveness of the proposed method.

Head-shoulder based gender recognition

The present invention relates to a method and a system for switching terminals among multiple wireless APs, wherein, the multiple wireless APs are connected to a central management AP. The method includes: receiving network condition information of a terminal in communication with a first wireless AP in the multiple wireless APs; according to the network condition information of the terminal, determining a second wireless AP as a switch target in the multiple wireless APs, wherein the connection between the second wireless AP and the terminal is a virtual connection; submitting a switch request to the second wireless AP; and in response to receiving a switch response from the second wireless AP, informing the terminal to switch to the second wireless AP. By the method and system, a mobile terminal may operate at better channel quality, thus improving network performance and throughput, and saving power consumption. At the same time, the disconnection during switching can be avoided.

Method and system for switching terminals among multiple wireless access points (AP)

Due to its safest operation and lowest expense, the fuel pipeline system plays crucial role in oil/gas transportation. In the pipe network industry, there are diverse optimization tasks during any period from design to operation. Previously, most works focus on developing effective algorithmic variants to strengthen some aspects of search capability. However, few review papers have been reported, which are desiderata for engineering solution providers. In this paper, we try to fulfill this gab by reviewing the existing optimization tasks and classifying them according two kinds of attributes. The problem formulation, major methods of three typical optimization tasks are reviewed and analyzed. The challenges and potential research directions of these problems are also analyzed.

A survey on oil / gas pipeline optimization: Problems, methods and challenges

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