A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

AbstractIn this work, a Multi-Objective Evolutionary Algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms; this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies.

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Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms

Evolutionary Computation ◽

10.1162/106365603766646816 ◽

2003 ◽

Vol 11 (2) ◽

pp. 151-167 ◽

Cited By ~ 133

Author(s):

Andrea Toffolo ◽

Ernesto Benini

Keyword(s):

Genetic Diversity ◽

Evolutionary Algorithms ◽

Evolutionary Algorithm ◽

Selection Pressure ◽

Evaluation Method ◽

State Of The Art ◽

Search Space ◽

Test Problems ◽

Multi Objective ◽

Diversity Evaluation

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.

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Combining exhaustive search and multi-objective evolutionary algorithm for service restoration in large-scale distribution systems

Electric Power Systems Research ◽

10.1016/j.epsr.2015.12.003 ◽

2016 ◽

Vol 134 ◽

pp. 1-8 ◽

Cited By ~ 14

Author(s):

Marcos H.M. Camillo ◽

Rodrigo Z. Fanucchi ◽

Marcel E.V. Romero ◽

Telma Woerle de Lima ◽

Anderson da Silva Soares ◽

...

Keyword(s):

Evolutionary Algorithm ◽

Distribution Systems ◽

Large Scale ◽

Exhaustive Search ◽

Multi Objective ◽

Service Restoration

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Improving Clustering Methods By Exploiting Richness Of Text Data

10.26686/wgtn.17019287.v1 ◽

2021 ◽

Author(s):

◽

Abdul Wahid

Keyword(s):

Evolutionary Algorithm ◽

State Of The Art ◽

Ensemble Methods ◽

Text Clustering ◽

Clustering Methods ◽

Clustering Method ◽

Clustering Ensemble ◽

Text Data ◽

Multi Objective ◽

User Queries

<p>Clustering is an unsupervised machine learning technique, which involves discovering different clusters (groups) of similar objects in unlabeled data and is generally considered to be a NP hard problem. Clustering methods are widely used in a verity of disciplines for analyzing different types of data, and a small improvement in clustering method can cause a ripple effect in advancing research of multiple fields. Clustering any type of data is challenging and there are many open research questions. The clustering problem is exacerbated in the case of text data because of the additional challenges such as issues in capturing semantics of a document, handling rich features of text data and dealing with the well known problem of the curse of dimensionality. In this thesis, we investigate the limitations of existing text clustering methods and address these limitations by providing five new text clustering methods--Query Sense Clustering (QSC), Dirichlet Weighted K-means (DWKM), Multi-View Multi-Objective Evolutionary Algorithm (MMOEA), Multi-objective Document Clustering (MDC) and Multi-Objective Multi-View Ensemble Clustering (MOMVEC). These five new clustering methods showed that the use of rich features in text clustering methods could outperform the existing state-of-the-art text clustering methods. The first new text clustering method QSC exploits user queries (one of the rich features in text data) to generate better quality clusters and cluster labels. The second text clustering method DWKM uses probability based weighting scheme to formulate a semantically weighted distance measure to improve the clustering results. The third text clustering method MMOEA is based on a multi-objective evolutionary algorithm. MMOEA exploits rich features to generate a diverse set of candidate clustering solutions, and forms a better clustering solution using a cluster-oriented approach. The fourth and the fifth text clustering method MDC and MOMVEC address the limitations of MMOEA. MDC and MOMVEC differ in terms of the implementation of their multi-objective evolutionary approaches. All five methods are compared with existing state-of-the-art methods. The results of the comparisons show that the newly developed text clustering methods out-perform existing methods by achieving up to 16\% improvement for some comparisons. In general, almost all newly developed clustering algorithms showed statistically significant improvements over other existing methods. The key ideas of the thesis highlight that exploiting user queries improves Search Result Clustering(SRC); utilizing rich features in weighting schemes and distance measures improves soft subspace clustering; utilizing multiple views and a multi-objective cluster oriented method improves clustering ensemble methods; and better evolutionary operators and objective functions improve multi-objective evolutionary clustering ensemble methods. The new text clustering methods introduced in this thesis can be widely applied in various domains that involve analysis of text data. The contributions of this thesis which include five new text clustering methods, will not only help researchers in the data mining field but also to help a wide range of researchers in other fields.</p>

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Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm

Biology ◽

10.3390/biology10121287 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1287

Author(s):

Xingsi Xue ◽

Pei-Wei Tsai ◽

Yucheng Zhuang

Keyword(s):

Evolutionary Algorithm ◽

Large Scale ◽

Biomedical Ontology ◽

Biomedical Data ◽

Biomedical Ontologies ◽

Ontology Alignment ◽

Multi Objective Optimization ◽

Matching Problem ◽

Multi Objective ◽

Matching Techniques

To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment’s quality, which calculate the alignment’s statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the biomedical ontology matching problem. By analyzing the essence of this problem, we point out that it is a large-scale Multi-modal Multi-objective Optimization Problem (MMOP) with sparse Pareto optimal solutions. Then, we propose a problem-specific aMMOEA to solve this problem, which uses the Guiding Matrix (GM) to adaptively guide the algorithm’s convergence and diversity in both objective and decision spaces. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s biomedical tracks to test aMMOEA’s performance, and comparisons with two state-of-the-art MOEA-based matching techniques and OAEI’s participants show that aMMOEA is able to effectively determine diverse solutions for decision makers.

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Detecting IoT Devices and How They Put Large Heterogeneous Networks at Security Risk

Sensors ◽

10.3390/s19194107 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4107

Author(s):

Sharad Agarwal ◽

Pascal Oser ◽

Stefan Lueders

Keyword(s):

Large Scale ◽

Nuclear Research ◽

The Internet ◽

Security Risk ◽

European Organization ◽

Security Issues ◽

Large Scale Network ◽

The World ◽

Iot Devices ◽

The Way

The introduction of the Internet of Things (IoT), i.e., the interconnection of embedded devices over the Internet, has changed the world we live in from the way we measure, make calls, print information and even the way we get energy in our offices or homes. The convenience of IoT products, like closed circuit television (CCTV) cameras, internet protocol (IP) phones, and oscilloscopes, is overwhelming for end users. In parallel, however, security issues have emerged and it is essential for infrastructure providers to assess the associated security risks. In this paper, we propose a novel method to detect IoT devices and identify the manufacturer, device model, and the firmware version currently running on the device using the page source from the web user interface. We performed automatic scans of the large-scale network at the European Organization for Nuclear Research (CERN) to evaluate our approach. Our tools identified 233 IoT devices that fell into eleven distinct device categories and included 49 device models manufactured by 26 vendors from across the world.

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MOEA/D for Multi-Objective Hybrid Flowshop Rescheduling Problem

Volume 4: Processes ◽

10.1115/msec2018-6581 ◽

2018 ◽

Author(s):

Biao Zhang ◽

Quan-ke Pan ◽

Liang Gao ◽

Yao-bang Zhao

Keyword(s):

Evolutionary Algorithm ◽

Completion Time ◽

Production Efficiency ◽

State Of The Art ◽

Weighted Sum ◽

Computational Results ◽

Hybrid Flowshop ◽

Multi Objective ◽

Effectiveness And Efficiency ◽

Maximum Completion Time

In this paper, a multi-objective hybrid flowshop rescheduling problem (HFRP) is addressed in a dynamic shop environment where two types of real-time events, namely machine breakdown and job cancellation, simultaneously happen. For the addressed problem, two objectives are considered. One objective concerning the production efficiency is minimizing the maximum completion time or makespan, while regarding with the instability, the total number of the jobs assigned to different machines between the revised and the origin schedule is considered. A multi-objective evolutionary algorithm based on decomposition (MOEA/D) is applied to solve this problem. In the algorithm, the weighted sum approach is used as the decomposition strategy. The algorithm is, then, rigorously compared with three state-of-the-art evolutionary multi-objective optimizers, and the computational results demonstrate the effectiveness and efficiency of the algorithm.

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