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Design of MIMO fractional order fault tolerant control based on intelligent QFT controller of robotic manipulator

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2024年第4期44卷 303-314页

作者： Yousfi-Allagui, Najah Aribi, Asma Aljuaid, Awad M. Taif Univ Coll Engn POB 11099 Taif 21944 Saudi Arabia Univ Sfax Engn Sch Control & Energy Management Lab CEM BP W Sfax 3038 Tunisia Taif Univ Coll Engn Dept Elect Engn POB 11099 Taif 21944 Saudi Arabia Univ Gabes Natl Engn Sch Gabes Res Unit Modeling Anal & Control Syst MACS 06-UR-11-12 Gabes Tunisia

Control systems that ensure safety, reliability and maintainability are challenging tasks to remedy the problems caused by unexpected faults. The quantitative-feedback-theory is efficient control method used for MIMO-systems with parametric uncertainties. Besides, the robust fractional-PID controller and fractional pre-filter demonstrated their efficiency to ensure robust control and performance specifications. The novelty consists of associating the benefits of fractional control and the QFT approach to develop a fractional-tolerant-controller for MIMO systems. A fractional fault-tolerant controller (FFTC) with some special characteristics allowing to overcome the design complexity and control effort increase due to simultaneous presence of disturbance with jumping faults is developed. Additionally, high-tracking performance is taken into consideration. The proposed approach is designing a controller with structure that converts the faults and the interaction in MIMO system to standard QFT disturbance rejection problem and guarantees high performance thanks to fractional approach. The proposed approach is applied to SCARA robot manipulator.

关键词： evolutionary algorithms fault tolerant control FTC fractional control multi-criteria optimisation quantitative feedback theory QFT robotic systems

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DMRFO-CD: a discrete manta ray foraging inspired optimisation algorithm for community detection in networks

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2024年第6期27卷 718-733页

作者： Gupta, Priyanka Gupta, Shikha Kumar, Naveen Univ Delhi Mata Sundri Coll Women Dept Comp Sci Delhi India Univ Delhi Shaheed Sukhdev Coll Business Studies Dept Comp Sci Delhi India Univ Delhi Dept Comp Sci Delhi India

There has been considerable interest in using bio-inspired evolutionary algorithms to detect communities in networks. Manta ray foraging optimisation (MRFO), a recently proposed real-valued bio-inspired evolutionary algorithm, has demonstrated superior performance in challenging complex optimisation problems. The present proposal is significant in its adaptation of the MRFO algorithm for the discrete-valued community detection problem. The proposed approach leverages the strengths of the MRFO algorithm for superior results through better exploration of the search space. The proposed approach maximises network modularity, a measure of connection density within a community. Higher modularity community structures find usefulness in uncovering meaningful information in the network. Experiments on real-world and synthetic benchmark networks show that the proposed approach successfully detects community structures with high modularity. We experimented on both real-world and synthetic benchmark networks. In two-thirds of the cases, the proposed algorithm achieved a higher modularity. For the remaining networks, the modularity achieved by the proposed approach was the same as that of the label.

关键词： evolutionary algorithms network modularity normalised mutual information metaheuristic swarm-based partitioning

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Integrated Software Effort Estimation: a Hybrid Approach

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第3期48卷 329-344页

作者： Singal, Prerna Sharma, Prabha Kumari, A. Charan The NorthCap University Gurugram India Dayalbagh Educational Institute Dayalbagh Agra India

Risks associated with delivery of a software project and the effort spent on managing these risks are well researched topics. Very few have included this extra effort termed as risk exposure of a project, in the software effort estimate of a project. This research proposes to improve the accuracy of software effort estimates by integrating the risk exposure with the initial effort estimate of the project. A function to calculate integrated effort estimates has been defined and evolutionary algorithms ABC, PSO and GLBPSO have been used to optimize the MMRE. The approach has been tested on two datasets collected from industry, one for waterfall projects, and another for agile projects. For both the datasets, integrated effort estimates were more accurate on account of MMRE, standardized accuracy, effect size and R2, than the initial effort estimates. evolutionary algorithms also gave the optimum weight values at which the MMRE was optimal for both the datasets. These weight values determine the contribution of risk associated with each project cost factor in the risk exposure of the project. Integrated effort estimates have been found to be more accurate, reliable, and comprehensive than the initial effort estimates. Application of evolutionary algorithms help in reducing any bias in the integrated effort estimates. © 2024 Slovene Society Informatika. All rights reserved.

关键词： ABC evolutionary algorithms GLBPSO PSO Risk exposure Risk management Software effort estimation

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evolutionary algorithm-based multiobjective reservoir operation policy optimisation under uncertainty

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ENVIRONMENTAL RESEARCH COMMUNICATIONS 2022年第12期4卷 121001页

作者： Wu, Wenyan Zhou, Yuerong Leonard, Michael Univ Melbourne Dept Infrastruct Engn Parkville Vic 3010 Australia Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia

Reservoir operation optimisation is a decision support tool to assist reservoir operators with water release decisions to achieve management objectives, such as maximising water supply security, mitigating flood risk, and maximising hydroelectric power generation. The effectiveness of reservoir operation decisions is subject to uncertainty in system inputs, such as inflow and therefore, methods such as stochastic dynamic programming (SDP) have been traditionally used. However, these methods suffer from the three curses of dimensionality, modelling, and multiple objectives. evolutionary algorithm (EA)-based simulation-optimisation frameworks such as the evolutionary Multi-Objective Direct Policy Search (EMODPS) offer a new paradigm for multiobjective reservoir optimisation under uncertainty, directly addressing the shortcomings of SDP-based methods. They also enable the consideration of input uncertainty represented using ensemble forecasts that have become more accessible recently. However, there is no universally agreed approach to incorporate uncertainty into EA-based multiobjective reservoir operation policy optimisation and it is not clear which approach is more effective. Therefore, this study conducts a comparative analysis to demonstrate the advantages and limitations of different approaches to account for uncertainty in multiobjective reservoir operation policy optimisation via a real-world case study;and provide guidance on the selection of appropriate approaches. Based on the results obtained, it is evident that each approach has both advantages and limitations. A suitable approach needs to be carefully selected based on the needs of the study, e.g., whether a hard constraint is required, or a well-established decision-making process exists. In addition, potential gaps for future research are identified.

关键词： reservoir operation policy optimisation uncertainty direct policy search evolutionary algorithms artificial neural networks

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Employing evolutionary algorithms for classification of astrophysical spectra 14

Employing evolutionary algorithms for classification of astr...

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14th Conference on Information Technologies - Applications and Theory, ITAT 2014

作者： Bednárek, David Kruliš, Martin Yaghob, Jakub Zavoral, Filip Parallel Architectures/Algorithms/Applications Research Group Faculty of Mathematics and Physics Charles University in Prague Malostranské nám. 25 Prague Czech Republic

ISBN: (纸本)9788087136188

In the past decade, automated astronomical observatories collected huge amounts of data which can no longer be explored by astronomers individually. In our case, we deal with optical spectra produced by multi-object low-resolution spectrographs. Due to lower resolution and higher level of noise in such surveys, individual spectra rarely offer reliable information;however, since many similar objects expectedly exist in the universe, global analysis of the spectrum database may reveal classes of objects sharing similar properties. In this paper, we propose a novel evolutionary approach to classification of spectral data which is expected to achieve finer level of detail than traditional methods. Furthermore, we describe the most computationally-intensive parts of the method in the form of parallel cache-aware algorithm. © 2014 D. Bednárek, M. Kruliš, J. Yaghob, F. Zavoral.

关键词： evolutionary algorithms

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Optimising Delivery Routes Under Real-World Constraints: A Comparative Study of Ant Colony, Particle Swarm and Genetic algorithms

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第10期15卷 796-803页

作者： Aldoraibi, Rneem I. Alanazi, Fatimah Alaskar, Haya Alanazi, Abed Pince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj Dept Comp Sci POB 151 Alkharj 11942 Saudi Arabia

logistics systems are essential for fast and economical package delivery, especially in urban areas. The intricate and ever-changing nature of urban logistics makes traditional methods insufficient. Hence, requirements for the application of sophisticated optimisation techniques have increased. To optimise package delivery routes, this study compares the performance of three popular evolutionary algorithms: ant colony optimisation (ACO), particle swarm Optimisation (PSO), and genetic algorithms (GA). Finding the best algorithm to minimise delivery time and cost while taking into account real-world limitations, such as delivery priority. This guarantees that deliveries with a higher priority are prioritised over others, which may substantially impact route optimisation. We examine each algorithm to create the best possible route plans for delivery trucks using actual data. Several factors are employed to assess each algorithm's performance, including robustness to changes in environmental variables and computational efficiency-the simulation models delivery demands using actual data. Results indicate that ACO performed better in Los Angeles and Chicago, completing the shortest routes with respective distances of 126,254.18 and 59,214.68, indicating a high degree of flexibility in intricate urban layouts. With the best distance of 48,403.1 in New York, on the other hand, GA achieve good results, demonstrating its usefulness in crowded urban settings. These results highlight how incorporating evolutionary algorithms into urban logistics can improve sustainability and efficiency.

关键词： evolutionary algorithms genetic algorithm particle swarm optimisation ant colony optimisation urban logistics route optimisation

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A critical take on the role of random and local search-oriented components of modern computational intelligence-based optimization algorithms

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Soft Computing 2024年第13-14期28卷 7963-7981页

作者： Zolghadr-Asli, Babak Sustainable Minerals Institute The University of Queensland Brisbane Australia The Centre for Water Systems The University of Exeter Exeter United Kingdom

The concept of computational intelligence (CI)-based optimization algorithms emerged in the early 1960s as a more practical approach to the contemporary derivate-based approaches. This paved the way for many modern algorithms to arise with an unprecedented growth rate in recent years, each claiming to have a novel and present a profound breakthrough in the field. That said, many have raised concerns about the performance of these algorithms and even identified fundamental flaws that could potentially undermine the integrity of their results. On that note, the premise of this study was to replicate some of the more prevalent, fundamental components of these algorithms in an abstract format as a measure to observe their behavior in an isolated environment. Six pseudo algorithms were designed to create a spectrum of intelligence behavior ranging from absolute randomness to local search-oriented computational architecture. These were then used to solve a set of centered and non-centered benchmark suites to see if statistically different patterns would emerge. The obtained result clearly highlighted that the algorithm’s performance would suffer significantly as these benchmarks got more intricate. This is not just in terms of the number of dimensions in the search space but also the mathematical structure of the benchmark. The implication is that, in some cases, sheer processing resources can mask the algorithm’s lack of sufficient intelligence. But as importantly, this study attempted to identify some mechanics and concepts that could potentially cause or amplify this problem. For instance, the excessive use of greedy strategy, a prevalent measure embedded in many modern CI-based algorithms, has been identified as potentially one of these reasons. The result, however, highlights a more fundamental problem in the CI-based optimization field. That is, these algorithms are often treated as a black box. This perception cultivated the culture of not exploring the underlying

关键词： evolutionary algorithms

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evolutionary Neural Architecture Search for Automatic Esophageal Lesion Identification and Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第3期3卷 436-450页

作者： Zhou, Yao Yuan, Xianglei Zhang, Xiaozhi Liu, Wei Wu, Yu Yen, Gary G. Hu, Bing Yi, Zhang Sichuan University Center of Intelligent Medicine College of Computer Science Chengdu610065 China Sichuan University Department of Gastroenterology West China Hospital Chengdu610065 China

Automatic esophageal lesion identification (ESEI) is of great importance to clinically aid the endoscopists with the early detection of esophageal cancer. However, accurate identification of esophageal lesion is challenging due to the varying shape, size, illumination condition, and complex background with artifacts in endoscopic images. Although deep neural network based approaches have considerably boosted the performance by automatically learning features from esophageal images, the configuration of the network architecture is highly dependent on domain expertise and is a daunting task to be manually tuned. In this article, we propose an evolutionary algorithm based approach to search for the optimal multitask network architecture for ESEI. Different from existing studies, we first design a multitask network search space, which considers the lesion identification as two steps including esophageal image classification and esophageal lesion segmentation. In particular, the input image resolution is covered in the search space, and the classification utilizes both downsampled and upsampled features. Besides, to avoid scratch training of sampled network architectures in the evolutionary algorithm, the one-shot supernet strategy is developed for searching the optimal network architecture. Results from the performed experiments on a collected sizeable clinical esophageal image dataset show that the proposed method improves on the state of the art on all measured metrics. © 2020 IEEE.

关键词： evolutionary algorithms

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Achieving improved stability for automatic voltage regulation with fractional-order PID plus double-derivative controller and mountain gazelle optimizer

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2024年第7期12卷 2550-2565页

作者： Izci, Davut Abualigah, Laith Can, Ozay Andic, Cenk Ekinci, Serdar Batman Univ Dept Comp Engn Batman Turkiye Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon Univ Sains Malaysia Sch Comp Sci Minden 11800 Pulau Pinang Malaysia Sunway Univ Sch Engn & Technol Petaling Jaya Selangor Malaysia Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Recep Tayyip Erdogan Univ Dept Elect & Automat Rize Turkiye Istanbul Tech Univ Dept Elect Engn Istanbul Turkiye Univ Tabuk Artificial Intelligence & Sensing Technol AIST Re Tabuk 71491 Saudi Arabia

The stability of voltage in a power system is a critical factor that impacts the system's performance. Automatic voltage regulator system plays a vital role in maintaining stable voltage levels, ensuring efficient and reliable electricity delivery. However, this system may face challenges, such as oscillating transient response, steady-state errors, and load variations. To overcome these limitations, various control techniques have been proposed, with proportional-integral-derivative controllers being the most commonly used. However, this research aims to optimize the parameters of the fractional-order proportional-integral-derivative plus double-derivative (FOPIDD2) controller for the automatic voltage regulator system. The proposed controller's six parameters are tuned using a novel evolutionary algorithm technique, the mountain gazelle optimizer, for the first time. The performance of the FOPIDD2 controller, tuned with mountain gazelle optimizer, is compared to that of other controllers which were optimized using different optimization techniques in the literature, as well as 13 studies with different controller approaches. The results demonstrate that the proposed mountain gazelle optimizer-based FOPIDD2 controller outperforms previously published optimization methods in the literature, leading to improvements in transient responses, such as settling time, rise time, and maximum overshoot. The implementation of the proposed approach is also demonstrated in a real-world setting and the robustness analysis is performed which further confirm the efficacy of the proposed approach.

关键词： Automatic voltage regulator Mountain gazelle optimizer FOPIDD2 controller evolutionary algorithms

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Simultaneously feature selection and parameters optimization by teaching-learning and genetic algorithms for diagnosis of breast cancer

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2024年 1-22页

作者： Shukla, Alok Kumar Thapar Inst Engn & Technol Patiala Punjab India

Currently, development of early and accurate breast cancer (BC) prediction models using computer-aided tools has proven to be beneficial, which in turn low mortality rate related to this disease. However, feature selection (FS) is a challenging task for the identification and characterization of cancers that increase the susceptibility to common complex multifactorial BC diseases, especially when dealing with clinical treatment. Most of the previous FS techniques does not handle important characterization such as removing irrelevant and/or redundant features separately. According to the past research on FS, several evolutionary algorithms have been proposed to address FS problems, but they have to fail for classifying BC survival types. In order to address before-mentioned issues, numerous hybridized models have been intended for selecting best features in effort to increase the accuracy of breast cancer predictive models. It may be cumbersome to obtain the perfect parameters for optimal performance. To resolve the deficiencies of past diagnostic system, in this paper, hybrid teaching-learning optimization (TLBO) and genetic algorithm (GA)-based is proposed consistent wrapper strategy called TLBOG to improve the reliability of evolutionary algorithms. The aim of using GA here is to tackle slow convergence rate and improve exploitation search capability found by TLBO. Most importantly, goal of our approach is to optimize the parameters of support vector machines to have high accuracy in contrast to other machine learning models and select best features subset simultaneously. From the performance evaluation results, we understand that proposed approach is significantly higher than conventional wrapper techniques in terms of accuracy, sensitivity, precision, and F-measure in the WBCD and WDBC databases.

关键词： Breast cancer TLBO Feature selection GA evolutionary algorithms Support vector machine

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