School of Computer Science and Informatics

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Browsing School of Computer Science and Informatics by Type "Technical Report"

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    Analysing the Moodle e-learning platform through subgroup discovery algorithms based on evolutionary fuzzy systems
    (DMU, 2012-09-01) Carmona, C. J.; Elizondo, David
    Nowadays, there is a increasing in the use of learning management systems from the universities. This type of systems are also known under other di erent terms as course management systems or learning content management systems. Speci cally, these systems are e-learning platforms o ering di erent facilities for information sharing and communication between the participants in the e-learning process. This contribution presents an experimental study with several subgroup discovery algorithms based on evolutionary fuzzy systems using data from a web-based education system. The main objective of this contribution is to extract unusual subgroups to describe possible relationships between the use of the e-learning platform and marks obtained by the students. The results obtained by the best performing algorithm, NMEEF-SD, are also presented. The most representative results obtained by this algorithm are summarised in order to obtain knowledge that can allow teachers to take actions to improve student performance.
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    Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization
    (2024-02) Luo, Wenjian; Xu, Peilan; Yang, Shengxiang; Shi, Yuhui
    The competition focuses on Multiparty Multiobjective Optimization Problems (MPMOPs), where multiple decision makers have conflicting objectives, as seen in applications like UAV path planning. Despite their importance, MPMOPs remain understudied in comparison to conventional multiobjective optimization. The competition aims to address this gap by encouraging researchers to explore tailored modeling approaches. The test suite comprises two parts: problems with common Pareto optimal solutions and Biparty Multiobjective UAV Path Planning (BPMO-UAVPP) problems with unknown solutions. Optimization algorithms for the first part are evaluated using Multiparty Inverted Generational Distance (MPIGD), and the second part is evaluated using Multiparty Hypervolume (MPHV) metrics. The average algorithm ranking across all problems serves as a performance benchmark.
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    Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments
    (2022-01) Luo, Wenjian; Lin, Xin; Li, Changhe; Yang, Shengxiang; Shi, Yuhui
    Dynamic and multimodal features are two important properties and widely existed in many real-world optimization problems. The former illustrates that the objectives and/or constraints of the problems change over time, while the latter means there is more than one optimal solution (sometimes including the accepted local solutions) in each environment. The dynamic multimodal optimization problems (DMMOPs) have both of these characteristics, which have been studied in the field of evolu tionary computation and swarm intelligence for years, and attract more and more attention. Solving such problems requires optimization algorithms to simultaneously track multiple optima in the changing environments. So that the decision makers can pick out one optimal solution in each environment according to their experiences and preferences, or quickly turn to other solutions when the current one cannot work well. This is very helpful for the decision makers, especially when facing changing environments. In this competition, a test suit about DMMOPs is given, which models the real-world applications. Specifically, this test suit adopts 8 multimodal functions and 8 change modes to construct 24 typical dynamic multimodal optimization problems. Meanwhile, the metric is also given to measure the algorithm performance, which considers the average number of optimal solutions found in all environments. This competition will be very helpful to promote the development of dynamic multimodal optimization algorithms.
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    Benchmark Functions for the CEC'2017 Competition on Many-Objective Optimization
    (University of Birmingham, U.K., 2017-01) Cheng, Ran; Li, Miqing; Tian, Ye; Zhang, Xingyi; Yang, Shengxiang; Jin, Yaochu; Yao, Xin
    In the real world, it is not uncommon to face an optimization problem with more than three objectives. Such problems, called many-objective optimization problems (MaOPs), pose great challenges to the area of evolutionary computation. The failure of conventional Pareto-based multi-objective evolutionary algorithms in dealing with MaOPs motivates various new approaches. However, in contrast to the rapid development of algorithm design, performance investigation and comparison of algorithms have received little attention. Several test problem suites which were designed for multi-objective optimization have still been dominantly used in many-objective optimization. In this competition, we carefully selects/designs 15 test problems with diverse properties, aiming to promote the research of evolutionary many-objective optimization (EMaO) via suggesting a set of test problems with a good representation of various real-world scenarios. Also, an open-source software platform with a user-friendly GUI is provided to facilitate the experimental execution and data observation.
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    Benchmark Functions for the CEC'2018 Competition on Dynamic Multiobjective Optimization
    (Newcastle University, 2018-01) Jiang, Shouyong; Yang, Shengxiang; Yao, Xin; Tan, Kay Chen; Kaiser, Marcus; Krasnogor, Natalio
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    Benchmark Functions for the CEC'2018 Competition on Many-Objective Optimization
    (2018-01) Cheng, Ran; Li, Miqing; Tian, Ye; Xiang, Xiaoshu; Zhang, Xingyi; Yang, Shengxiang; Jin, Yaochu; Yao, Xin
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    Benchmark generator for CEC 2009 competition on dynamic optimization
    (University of Leicester, U.K., 2008-10) Li, Changhe; Yang, Shengxiang; Nguyen, T. T.; Ling Yu, E.; Yao, Xin; Jin, Yaochu; Beyer, H. -G.; Suganthan, P. N.
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    Benchmark Generator for the IEEE WCCI-2012 Competition on Evolutionary Computation for Dynamic Optimization Problems
    (Brunel University, U.K., 2011-10) Li, Changhe; Yang, Shengxiang; Pelta, David A.
    Based on our previous benchmark generator for the IEEE CEC’09 Competition on Dynamic Optimization, this report updates the two benchmark instances where a new change type has been developed as well as a constraint to the benchmark instance of the dynamic rotation peak benchmark generator.
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    Benchmark generator for the IEEE WCCI-2012 competition on evolutionary computation for dynamic optimization problems. Technical Report 2011.
    (Department of Information Systems and Computing, Brunel University., 2011) Li, Changhe; Yang, Shengxiang; Pelta, David A.
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    Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Rotation Peak Benchmark Generator (DRPBG) and Dynamic Composition Benchmark Generator (DCBG)
    (De Montfort University, UK, 2013-10) Li, Changhe; Mavrovouniotis, Michalis; Yang, Shengxiang; Yao, Xin
    Based on our previous benchmark generator for the IEEE CEC’12 Competition on Dynamic Optimization, this report updates the two benchmark instances where two new features have 1been developed as well as a constraint to the benchmark instance of the dynamic rotation peak benchmark generator. The source code in C++ language for the two benchmark instances is included in the library of EAlib, which is an open platform to test and compare the performances of EAs.
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    Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Travelling Salesman Problem Benchmark Generator
    (De Montfort University, U.K., 2013-10) Mavrovouniotis, Michalis; Li, Changhe; Yang, Shengxiang; Yao, Xin
    In this report, the dynamic benchmark generator for permutation-encoded problems for the travelling salesman problem (DBGPTSP) proposed in is used to convert any static travelling salesman problem benchmark to a dynamic optimization problem, by modifying the encoding of the instance instead of the fitness landscape.
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    Benchmark Problems for CEC2023 Competition on Dynamic Constrained Multiobjective Optimization
    (2022-12) Gui, Yinan; Chen, Guoyu; Yue, Caitong; Liang, Jing; Wang, Yong; Yang, Shengxiang
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    Benchmark Problems for IEEE WCCI2024 Competition on Dynamic Constrained Multiobjective Optimization
    (2023-12) Guo, Yinan; Chen, Guoyu; Yue, Caitong; Liang, Jing; Wang, Yong; Yang, Shengxiang
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    Common Criteria Protection Profile for Secure Communication Module for Water Tracking System
    (Common Criteria Portal, 2015-10-15) Bingol, Muhammed Ali; Kocabas, Unal; Kardas, Suleyman
    The Target of Evaluation (TOE) as defined in this Protection Profile is the Secure Communication Module for Water Tracking System (WTS). The TOE collects information from input devices such as pH sensor, conductivity sensor, temperature sensor, flow meter, RFID / 2D barcode reader, etc., and then it sends these collected data to the Data Management Center (DMC). In this section, first the overall Water Tracking System is introduced. Then, details of Secure Communication Module (TOE) are given. Afterwards the components of TOE, the cryptographic operations performed by TOE and the capabilities of TOE are introduced.
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    Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)
    (2023-12) Yazdani, Danial; Mavrovouniotis, Michalis; Li, Changhe; Luo, Wenjian; Omidvar, Mohammad Nabi; Gandomi, Amir H.; Nguyen, Trung Thanh; Branke, Juergen; Li, Xiaodong; Yang, Shengxiang; Yao, Xin
    This document introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating continuous dynamic optimization problem instances that is used for the CEC 2024 Competition on Dynamic Optimization. GMPB is adept at generating landscapes with a broad spectrum of characteristics, offering everything from unimodal to highly multimodal landscapes and ranging from symmetric to highly asymmetric configurations. The landscapes also vary in texture, from smooth to highly irregular surfaces, encompassing diverse degrees of variable interaction and conditioning. This document delves into the intricacies of GMPB, detailing the myriad ways in which its parameters can be tuned to produce these diverse landscape characteristics. GMPB's MATLAB implementation is available on the EDOLAB Platform.
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    Deriving Real-Time Programs from Duration Calculus Specifications
    (Technical Report 222, UNU/IIST, P.O. Box 3058, 2000) Siewe, Francois; Van Hung, Dang
    In this paper we present a syntactical approach for deriving real-time programs from a formal specification of the requirements of real-time systems. The main idea of our approach is to model discretization at state level by introducing the discrete states approximating the continuous ones, and then derive a specification of the control program over discrete states. Then the control program is derived from its specification using an extension of Hoare triples to real-time.
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    Experimental evaluation of algorithmic solutions for the maximum generalised network flow problem
    (2001-12) Radzik, Tomasz; Yang, Shengxiang
    The maximum generalised network flow problem is to maximise the net flow into a specified node in a network with capacities and gain-loss factors associated with edges. In practice, input instances of this problem are usually solved using general-purpose linear programming codes, but this may change because a number of specialised combinatorial generalised-flow algorithms have been recently proposed. To complement the known theoretical analyses of these algorithms, we develop their implementations and investigate their actual performance. We focus in this study on Goldfarb, Jin and Orlin's excess-scaling algorithm and Tardos and Wayne's push-relabel algorithm. We develop variants of these algorithms to improve their practical efficiency. We compare the performance of our implementations with implementations of simple, but non-polynomial, combinatorial algorithms proposed by Onaga and Truemper, and with performance of CPLEX, a commercial general-purpose linear programming package.
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    Fuzzy Photo Project
    (2011-09-28) Greenfield, Sarah
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    GaAs FETs at Frequencies above 20GHz
    (Plessey, 1982-10) Oxley, C. H.; Forrest, P.; Bennett, R. H.
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    Guidelines for Responsible Research and Innovation
    (De Montfort University, 2016) Wilford, S.; Fisk, Malcolm; Stahl, Bernd Carsten, 1968-
    Guidelines for Responsible Research and Innovation