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A review of feature selection methods based on meta-heuristic algorithms

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2025年第1期37卷 1-51页

作者： Sadeghian, Zohre Akbari, Ebrahim Nematzadeh, Hossein Motameni, Homayun Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

Feature selection is a real-world problem that finds a minimal feature subset from an original feature set. A good feature selection method, in addition to selecting the most relevant features with less redundancy, can also reduce computational costs and increase classification performance. One of the feature selection approaches is using meta-heuristic algorithms. This work provides a summary of some meta-heuristic feature selection methods proposed from 2018 to 2022 that were designed and implemented on a wide range of different data for solving feature selection problem. Evaluation criteria, fitness functions and classifiers used and the time complexity of each method are also depicted. The results of the study showed that some meta-heuristic algorithms alone cannot perfectly solve the feature selection problem on all types of datasets with an acceptable speed. In other words, depending on dataset, a special meta-heuristic algorithm should be used. The results of this study and the identified research gaps can be used by researchers in this field.

关键词： Data dimension reduction classification feature selection optimisation algorithm meta-heuristic algorithms

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optimisation OF THE MAIN HULL SUBDIVISION AND LAYOUT BASED ON TOPOLOGY-GEOMETRY CO-CHARACTERISATION 43

OPTIMISATION OF THE MAIN HULL SUBDIVISION AND LAYOUT BASED O...

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ASME 43rd International Conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Yu, Lei Wang, Hao Luo, Junting Chen, Shunhuai Wuhan Univ Technol Wuhan Peoples R China Nanning Port & Nav Dev Ctr Nanning Peoples R China

ISBN: (纸本)9780791887837

The subdivision and layout of a ship's main hull is a critical aspect of ship design as it directly determines the layout of the ship's space. To enhance design efficiency and quality, an intelligent optimization method for main hull subdivision is proposed. This method aims to reduce the traditional manual subdivision design process, which heavily relies on mother ship data, and address issues such as long design cycles and high costs. The text describes the sorting and analysis of the mutual influence relationship between ship compartments and equipment. A topological characterization model of the influence relationship and an automatic identification method are proposed. The topological objective function of favourable influences between compartments and equipment is constructed, and the geometrical constraint model is proposed based on the design specifications and design experience. The Diffusion algorithm Based on P Systems(DAPS) is then employed to achieve the optimization solution, resulting in a collection of intelligent optimization methods for main hull compartmentalization. The test for optimizing the main hull subdivision design was conducted on a scientific research vessel. The iteration curve for the optimization objective showed good convergence, and the optimization had a positive impact on the parent ship subdivision scheme. The test results demonstrate the method's rationality.

关键词： Main hull subdivision Topology geometry Network science Probability of ship damage optimisation algorithm

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Innovative design methods for the geometric accuracy of machine tool guide rail oriented to spatial accuracy

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JOURNAL OF MANUFACTURING PROCESSES 2024年 119卷 483-498页

作者： Wang, Sitong He, Gaiyun Zhang, Dawei Chen, Faze Yao, Chenglin Yan, Yichen Tianjin Univ Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300354 Peoples R China

The design of geometric accuracy in machine tools is crucial for enhancing machining precision. This study introduces geometric accuracy design methods that are oriented to spatial accuracy to guide the assembly of machine tool guide rails. Firstly, based on screw theory, a spatial error model for the machine tool is constructed, elucidating the relationship between spatial error and geometric error. Secondly, an error transmission model linking the guide rail installation error with the geometric error of moving parts is established, predicated on the rigid body hypothesis. This model reveals the mechanism of the error homogenisation in the machine tool feed system. Then, novel geometric accuracy design methods oriented to spatial accuracy are proposed catering to both non-morphological and morphological design requirements. Finally, the effectiveness and universality of the design methods are verified by simulations and experiments. The results show that more than 95.62 % of the machine tool workspace simulation points are less than 15 mu m in spatial accuracy, and the design results outperform the experimental results by 2.05 mu m in terms of the body diagonal spatial error.

关键词： Spatial accuracy Geometric accuracy Error homogenisation mechanism Accuracy design methods optimisation algorithm

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From simulation to reality: CFD-ML-driven structural optimization and experimental analysis of thermal plasma reactors

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2024年第3期12卷

作者： Shi, Hao-yang Wang, Shu Wang, Ping-yang Shanghai Jiao Tong Univ Inst Engn Thermophys Sch Mech & Power Engn Shanghai Peoples R China

Thermal plasma reactors offer an environment of high temperature, enthalpy, and reactivity, making them highly efficient for solid waste treatment and promising for clean energy production from municipal and industrial waste. Optimal geometrical parameters of the reactor can enhance waste treatment and reactor performance. This study presents a comprehensive analysis of 11 key geometrical parameters of a thermal plasma reactor. Utilizing CFD Fluent software, numerical simulations were conducted to generate a dataset. Subsequently, a predictive model focusing on the average temperature in the core melt zone was trained using six Machine Learning (ML) algorithms. The Particle Swarm optimisation (PSO) algorithm optimized the hyperparameters of the Gradient Booster Regression (GBR) model, which was combined with a Genetic algorithm (GA) to identify the reactor's optimal geometrical parameters. A DC arc plasma torch-solid waste thermal plasma reactor treatment system was established on this basis. The study also explored the effects of gasification coefficient, reaction temperature, and thermal plasma jet mode on system performance. Findings indicate that the PSO-GBR model achieved the highest prediction accuracy, with the temperature in the core reaction zone reaching 3621 K. The deviation between numerical simulations and machine learning predictions was a mere 1.3%. Enhancing syngas yield and energy efficiency is achievable by controlling reaction temperature and increasing the gasification coefficient. A laminar plasma jet mode, at equal power, provides a more effective reaction environment. The accuracy and reliability of the machine learning-driven regression model and optimization results are significant in guiding the optimal design of plasma reactors and advancing waste-to-energy conversion processes.

关键词： Thermal plasma Machine learning optimisation algorithm Reactor Waste treatment Experimental study

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Optimal Selection of Switch Model Parameters for ADC-Based Power Converters

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ENERGIES 2024年第1期17卷 56页

作者： Alsarayreh, Saif Suto, Zoltan Budapest Univ Technol & Econ Dept Automat & Appl Informat Muegyet Rkp 3 H-1111 Budapest Hungary

Real-time hardware-in-the-loop-(HIL) simulation integration is now a fundamental component of the power electronics control design cycle. This integration is required to test the efficacy of controller implementations. Even though hardware-in-the-loop-(HIL) tools use FPGA devices with computing power that is rapidly evolving, developers constantly need to balance the ease of deploying models with acceptable accuracy. This study introduces a methodology for implementing a full-bridge inverter and buck converter utilising the associate-discrete-circuit-(ADC) model, which is optimised for real-time simulator applications. Additionally, this work introduces a new approach for choosing ADC parameter values by using the artificial-bee-colony-(ABC) algorithm, the firefly algorithm (FFA), and the genetic algorithm (GA). The implementation of the ADC-based model enables the development of a consistent architecture in simulation, regardless of the states of the switches. The simulation results demonstrate the efficacy of the proposed methodology in selecting optimal parameters for an ADC-switch-based full-bridge inverter and buck converter. These results indicate a reduction in overshoot and settling time observed in both the output voltage and current of the chosen topologies.

关键词： associate discrete circuit real-time simulation field-programmable gate array optimisation algorithm power converters

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A survey, taxonomy and progress evaluation of three decades of swarm optimisation

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第5期55卷 3607-3725页

作者： Liu, Jing Anavatti, Sreenatha Garratt, Matthew Tan, Kay Chen Abbass, Hussein A. Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

While the concept of swarm intelligence was introduced in 1980s, the first swarm optimisation algorithm was introduced a decade later, in 1992. In this paper, nineteen representative original swarm optimisation algorithms are analysed to extract their common features and design a taxonomy for swarm optimisation. We use twenty-nine benchmark problems to compare the performance of these nineteen algorithms in the form they were first introduced in the literature against five state-of-the-art swarm algorithms. This comparison reveals the advancements made in this field over three decades. It reveals that, while the state-of-the-art swarm optimisation algorithms are indeed competitive in terms of the quality of solutions they find, their complexities have evolved to be more computationally demanding when compared to the nineteen original algorithms of swarm optimisation. The investigation suggests that there is an urge to continue to design swarm optimisation algorithms that are simpler, while maintaining their current competitive performance.

关键词： Swarm intelligence optimisation algorithm Taxonomy Evolutionary computation

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Design optimisation of low earth orbit constellation based on BeiDou Satellite Navigation System precise point positioning

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IET RADAR SONAR AND NAVIGATION 2022年第8期16卷 1241-1252页

作者： Liu, Jing Hao, Jinming Yang, Yan Xu, Zheyu Liu, Weiping Wu, Renzhe PLA Strateg Support Force Informat Engn Univ Sch Geospatial Informat Zhengzhou 450001 Henan Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan Peoples R China Sun Yat Sen Univ Sch Phys & Astron Zhuhai Guangdong Peoples R China Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu Peoples R China

The use of low earth orbit (LEO) satellites to enhance the performance of global navigation satellite system navigation and positioning services has become a popular research topic. In this study, NSGA-III optimisation algorithm was used to design two hybrid configurations of 177 and 186 LEO constellations for enhancing the BeiDou Satellite Navigation System (BDS). Under the enhanced effect of optimisation constellation, the global average geometric dilution of precision (GDOP) of BDS was reduced to 0.8 +/- 0.1, and the maximum GDOP was reduced from 2.4 to less than 1.1 (54.2% reduction). In order to verify the contribution of the two constellations to the convergence time and positioning accuracy of BDS precise point positioning (PPP), a LEO enhanced BDS PPP simulation experiment was carried out using International GNSS Service data from five stations. The results show that after 10 min of static positioning, both LEO constellations improved the positioning accuracy of BDS from the decimetre level to less than 5 cm. The maximum improvement for 177 and 186 LEO was 95.0% and 96.9%, respectively. Additionally, the convergence time for 177 and 186 LEO reduced to less than 3.5 and 3 min, and the maximum improvement was 93.5% and 95.2%, respectively. Overall, both constellations can improve the positioning accuracy and convergence time of BDS PPP.

关键词： Beidou satellite navigation system convergence time LEO satellite navigation enhancement optimisation algorithm precise point positioning

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Optimised Continually Evolved Classifier for Few-Shot Learning Overcoming Catastrophic Forgetting

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2025年第2期24卷

作者： Lichode, Rupatai Karmore, Swapnili GH Raisoni Univ Dept Comp Sci & Engn Borgaon 480337 Madhya Pradesh India

Catastrophic forgetting (CF) poses the most important challenges in neural networks for continual learning. During training, numerous current approaches replay precedent data that deviate from the constraints of an optimal continual learning system. In this work, optimisation-enabled continually evolved classifier is used for the CF. Moreover, a few-shot continual learning model is exploited to mitigate CF. Initially, images undergo pre-processing using Contrast Limited Adaptive Histogram Equalisation (CLAHE). Then;the pre-processed outputs are utilised in the classification through Continually Evolved Classifiers based on few-shot incremental learning. Here, the initial training is done by using the CNN (Convolutional Neural Network) model and then the pseudo incremental learning phase is performed. Furthermore, to enhance the performance, an optimisation approach, called Serial Exponential Sand Cat Swarm optimisation (SExpSCSO), is developed. SExpSCSO algorithm modifies Sand Cat Swarm optimisation by incorporating the serial exponential weighted moving average concept. The proposed SExpSCSO is applied to train the continually evolved classifier by optimising weights and thus, improves the classifiers performance. Finally, the experimentation analysis reveals that the adopted system acquired the maximal accuracy of 0.677, maximal specificity of 0.592, maximal precision of 0.638, recall of 0.716 and F-measure of 0.675.

关键词： Catastrophic forgetting few shot learning classifier CNN optimisation algorithm

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Inversion Method for Material Parameters of Concrete Dams Using Intelligent algorithm-Based Displacement Separation

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WATER 2024年第20期16卷 2979页

作者： Xu, Jianrong Gao, Lingang Li, Tongchun Guo, Jinhua Qi, Huijun Peng, Yu Wang, Jianxin Powerchina Huadong Engn Corp Ltd Hangzhou 311122 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Integrating long-term observational data analysis with numerical simulations of dam operations provides an effective approach to dam safety evaluation. However, analytical results are often subject to errors due to challenges in accurately surveying and modeling the foundation, as well as temporal changes in foundation properties. This paper proposes a concrete dam displacement separation model that distinguishes between deformation caused by foundation restraint and that induced by external loads. By combining this model with intelligent optimization techniques and long-term observational data, we can identify the actual mechanical parameters of the dam and conduct structural health assessments. The proposed model accommodates multiple degrees of freedom and is applicable to both two- and three-dimensional dam modeling. Consequently, it is well-suited for parameter identification and health diagnosis of concrete gravity and arch dams with extensive observational data. The efficacy of this diagnostic model has been validated through computational case studies and practical engineering applications.

关键词： concrete dam parameter inversion foundation constraints finite element method optimisation algorithm

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Swarm intelligence-based optimisation algorithms: an overview and future research issues

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2020年第5-6期14卷 656-693页

作者： Hu, Jinqiang Wu, Husheng Zhong, Bin Xiao, Renbin Armed Police Force Engn Univ Sch Equipment Management & Support Xian 710086 Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China

Swarm intelligence-based optimisation algorithms, inspired by the collective intelligent behaviours of biology groups, have been widely recognised as efficient optimisers for many complex problems, e.g., dynamic optimisation problems, large-scale optimisation problems and many-objective optimisation problems. Swarm intelligence-based algorithms are the generic concepts to represent a range of metaheuristics with population-based iterative process, guided random search and parallel processing. This paper conducts an in-depth analysis of universality and difference of existing swarm intelligence-based algorithms. It also provides a systematical survey of some well-known algorithms. In addition, the expected research issues such as theoretical analysis, hybridisation strategy and complex problems optimisation are discussed thoroughly to inspire future study and more extensive applications.

关键词： swarm intelligence optimisation algorithm universality theoretical analysis hybridisation strategy complex optimisation problems

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