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optimisation algorithms for OSNR measurement based on polarisation nulling

ELECTRONICS LETTERS 2015年第13期51卷 1007-1009页

作者： Feres, M. M. Floridia, C. Romero, M. A. Univ Sao Paulo EESC USP Sao Carlos SP Brazil CPqD Fdn Campinas SP Brazil

optimisation algorithms are demonstrated as a nulling finder for an in-band optical signal-to-noise ratio (OSNR) monitoring method based on polarisation extinction. The method was tested and validated in the Brazilian GIGA optical fibre testbed. When three optimisation techniques (genetic algorithm, particle swarm optimisation (PSO) and downhill simplex) were compared, PSO provided the best results regarding convergence time and nulling efficiency.

关键词： particle swarm optimisation genetic algorithms optical fibre communication light polarisation optimisation algorithms OSNR measurement polarisation nulling nulling finder in-band optical signal-to-noise ratio monitoring method Brazilian GIGA optical fibre testbed genetic algorithm particle swarm optimisation PSO downhill simplex DS convergence time nulling efficiency

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Detecting self-organising patterns in crowd motion: effect of optimisation algorithms

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JOURNAL OF MATHEMATICS IN INDUSTRY 2024年第1期14卷 6页

作者： Worku, Samson Mullick, Pratik Wroclaw Univ Sci & Technol Dept Operat Res & Business Intelligence Wroclaw Poland

The escalating process of urbanization has raised concerns about incidents arising from overcrowding, necessitating a deep understanding of large human crowd behavior and the development of effective crowd management strategies. This study employs computational methods to analyze real-world crowd behaviors, emphasizing self-organizing patterns. Notably, the intersection of two streams of individuals triggers the spontaneous emergence of striped patterns, validated through both simulations and live human experiments. Addressing a gap in computational methods for studying these patterns, previous research utilized the pattern-matching technique, employing the Nelder-Mead Simplex algorithm for fitting a two-dimensional sinusoidal function to pedestrian coordinates. This paper advances the pattern-matching procedure by introducing Simulated Annealing as the optimization algorithm and employing a two-dimensional square wave for data fitting. The amalgamation of Simulated Annealing and the square wave significantly enhances pattern fitting quality, validated through statistical hypothesis tests. The study concludes by outlining potential applications of this method across diverse scenarios.

关键词： Pedestrian dynamics Human-crowd motion Geometric patterns Stripe detection optimisation algorithms

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Model-Driven Engineering of Planning and optimisation algorithms for Pervasive Computing Environments

Model-Driven Engineering of Planning and Optimisation Algori...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Harrington, Anthony Cahill, Vinny Univ Dublin Trinity Coll Sch Comp Sci & Stat Distributed Syst Grp Dublin 2 Ireland

ISBN: (纸本)9781424495290

This paper presents a model-driven approach to developing pervasive computing applications that exploits design-time information to support the engineering of planning and optimisation algorithms that reflect the presence of uncertainty, dynamism and complexity in the application domain. In particular the task of generating code to implement planning and optimisation algorithms in pervasive computing domains is addressed. We present a layered domain model containing a set of object-oriented specifications for modelling physical and sensor/actuator infrastructure and state-space information. Our model-driven engineering approach is implemented in two transformation algorithms. The initial transformation parses the domain model and generates a planning model for the application being developed that encodes an application's states, actions and rewards. The second transformation parses the planning model and selects and seeds a planning or optimisation algorithm for use in the application. We present an empirical evaluation of the impact of our approach on the development effort associated with a pervasive computing application from the Intelligent Transportation Systems (ITS) domain, and provide a quantitative evaluation of the performance of the algorithms generated by the transformations.

关键词： Actuators Algorithm design and analysis Computational modeling Junctions Optimization Pervasive computing Planning actuator infrastructure actuators intelligent transportation systems domain layered domain model model-driven engineering approach optimisation optimisation algorithms pervasive computing environments planning model sensor infrastructure sensors state-space information traffic engineering computing ubiquitous computing

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Improvement by combination: How to increase the Performance of optimisation algorithms by combining them 10

Improvement by combination: How to increase the Performance ...

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10th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2014

作者： Ostler, Johannes Wilke, Peter University of Erlangen-Nuernberg Computer Science Department Multi Criteria Optimisation Group Martensstrasse 3 Erlangen91058 Germany

ISBN: (纸本)9780992998400

Working with different optimisation algorithms leads to the observation that different types of solutions are generated, disclosing their different nature, their pros and cons. We investigated the question whether or not the combination of optimisation algorithms yield even better results than each of its constituents or will their drawbacks make things even worse. © PATAT 2014 - Proceedings of the 10th International Conference on the Practice and Theory of Automated Timetabling. All rights reserved.

关键词： Combination of algorithms optimisation algorithms Performance Improvement

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Probabilistic online learning framework for short-term wind power forecasting using ensemble bagging regression model

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ENERGY CONVERSION AND MANAGEMENT 2025年 323卷

作者： Nayak, Arun Kumar Sharma, Kailash Chand Bhakar, Rohit Tiwari, Harpal Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India Dr BR Ambedkar Natl Inst Technol Dept Elect Engn Jalandhar Punjab India

The increasing penetration of renewable energy sources, with a notable focus on wind power, within modern electricity grids requires computationally efficient and burden-free short-term wind power forecasting models. Traditional models generating prediction intervals are trained offline and thus deployed for prediction purposes. However, this approach cannot obtain interval forecasts from the most recent wind power observations. In contrast, combining multiple regression models through ensemble learning is recognised as a successful method for improving forecasting performance. By utilising the most recent observations and exploiting the strengths of multiple regression models, this article investigates an Online Ensemble Bagging Regression (OEBR) model for generating prediction intervals. Online gradient descent based optimisation algorithms capable of adaptive-depth calculation from a stream of observations are used hereto address the problems with traditional batch learning frameworks. The proposed online learning framework is evaluated against other online learning frameworks using publicly accessible datasets. The results show the proposed model competes with the compared models regarding probabilistic metrics and energy estimations and outperforms computational time requirements for the same number of observations.

关键词： Batch learning framework Computationally efficient Ensemble bagging Online learning optimisation algorithms Prediction intervals Short-term wind power forecasting

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Analysis of different neural network models based on variational mode decomposition and dung beetle optimizer algorithm for the prediction of air-conditioning energy consumption in multifunctional complex large public buildings

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ENERGY AND BUILDINGS 2025年 334卷

作者： Liu, Jingtao Zhang, Yuxiang Wen, Kunbo Ding, Yunfei Guangzhou Univ Sch Civil Engn & Transportat Guangzhou 510006 Peoples R China Guangzhou Yuexiu Commercial Real Estate Management Guangzhou 510000 Peoples R China China Construct Third Engn Bur Grp Co Ltd Guangzhou 510600 Peoples R China

Forecasting daily air-conditioning energy consumption helps managers to plan system operations proactively, enhance the operational efficiency of units and equipment, and reduce overall energy consumption. Artificial neural network models have achieved remarkable results in the field of energy consumption prediction. However, some comprehensive large-scale public buildings, due to their complex functions, exhibit highly irregular daily variations in air-conditioning energy consumption, posing significant challenges for accurate prediction. In order to enhance the accuracy of predicting air-conditioning energy consumption in such buildings, and mitigate the issue of significant prediction errors in neural network models caused by abrupt changes in energy consumption over certain periods, the actual operational data from the central air-conditioning system of a largescale comprehensive public building in Guangzhou over the past two years has been collected. Based on this data, a novel prediction method for air-conditioning energy consumption is proposed. Based on Long short-term memory (LSTM), Back propagation algorithm (BP) and Gate recurrent unit (GRU) neural networks, combined with Variational Mode Decomposition (VMD) and Dung Beetle Optimizer (DBO), nine machine learning energy consumption prediction models were established, and the prediction effects before and after the optimisation of different models, as well as the consumption of computational resources by the models, were tested and compared. The results show that without optimization, LSTM, BP and GRU neural network models are easy to produce large errors, especially in the part of air conditioning energy consumption mutation, making it difficult for them to identify the change law of data. The optimised models significantly improve this situation and also improve the overall prediction accuracy. Compared with the pre-optimization models, the mean absolute percentage error (MAPE) of the optimized VMD-DBO-LS

关键词： Comprehensive large-scale public buildings Air-conditioning energy consumption prediction Local mutations optimisation algorithms Machine learning

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Multi-layers deep learning model with feature selection for automated detection and classification of highway pavement cracks

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SMART AND SUSTAINABLE BUILT ENVIRONMENT 2025年第2期14卷 511-535页

作者： Elghaish, Faris Matarneh, Sandra Abdellatef, Essam Rahimian, Farzad Hosseini, M. Reza Kineber, Ahmed Farouk Queens Univ Belfast Fac Engn & Phys Sci Belfast Antrim North Ireland Al Ahliyya Amman Univ Dept Civil Engn Amman Jordan Delta Higher Inst Engn & Technol Mansoura Egypt Teesside Univ Middlesbrough Cleveland England Teesside Univ Dept Engn Middlesbrough Cleveland England Deakin Univ Fac Sci Engn & Built Environm Dept Architecture & Built Environm Geelong Vic Australia Prince Sattam Bin Abdulaziz Univ Al Kharj Saudi Arabia

PurposeCracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway ***/methodology/approachTo enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained *** study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest *** implicationsWith an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to signi

关键词： CNN Highway surface cracks Feature selection optimisation algorithms Particle swarm optimisation (PSO)

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Evaluation of optimization algorithms for power and voltage in solar power generation system: FOPID controller and MPPT-based P&O algorithm

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International Journal of Information Technology (Singapore) 2025年 1-13页

作者： Dawood, Zahraa Ali Tackie, Samuel Nii Dimililer, Kamil Department of Electrical and Electronic Engineering Faculty of Engineering Near East University Nicosia Cyprus Ministry of Higher Education and Scientific Research Baghdad Iraq

In this work, the study gives attention for improvement of the Maximum Power Point Tracking (MPPT) using the Perturb and Observe (P&O) algorithm based MPPT applied to solar power generation system (SPGS). The algorithm components are refined, outcomes of the resultant optimized values are compared to address challenges in optimizing power, voltage and efficiency with the research. Further to improve this performance, Fractional Order Proportional Integral Derivative (FOPID) criteria are tuned using optimization techniques such as Black Hole (BH) Optimization, Jaya Optimization Algorithm (JOA), and Sunflower Optimization (SFO). Selection of optimal power and voltage values was aided by these methods. Limitations of the study include the exclusion of dynamic environmental changes in the analysis, and the potential non specificity of optimization algorithms for all SPGS scenarios considered. SPGS offers an opportunity to explore more dynamic models, as well as alternative algorithms, to improve the power and voltage management done in SPGS. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： FOPID controller MPPT optimisation algorithms P&O algorithm SPGS

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A structured review of large language models in metaheuristic optimisation

Decision Analytics Journal

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Decision Analytics Journal 2025年 15卷

作者： Reza Ghanbarzadeh Seyedali Mirjalili Faculty of Science and Engineering Southern Cross University Gold Coast Australia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Melbourne 3000 Australia Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava Czech Republic University Research and Innovation Center Obuda University Budapest 1034 Hungary

Metaheuristics are widely used to address complex optimisation problems where traditional exact methods are computationally infeasible or insufficiently flexible. With the rapid advancement of artificial intelligence, large language models, such as ChatGPT, Claude, Gemini, and LLaMA, have emerged as powerful tools capable of enhancing, automating, and adapting various stages of the optimisation process. This systematic literature review investigates the evolving role of large language models in metaheuristic optimisation, with a focus on algorithm generation, parameter tuning, hybridisation, constraint handling, and multi-objective optimisation. Following PRISMA guidelines, 25 studies from nine major scientific databases were selected and analysed. Through thematic analysis, a novel role-based taxonomy was developed that categorises large language model contributions into four functional roles: Advisor, Refiner, Enhancer, and Innovator. The findings demonstrate that large language models support the automation of metaheuristic workflows, enable dynamic adaptation, and contribute to the creation of novel heuristic strategies. Despite these advantages, the review also identifies persistent limitations, including prompt sensitivity, computational overhead, and scalability challenges. These issues highlight the need for more robust evaluation frameworks and benchmarking practices. This review offers a comprehensive synthesis of the current landscape, highlights research gaps, and provides actionable insights for researchers and practitioners aiming to integrate large language models into advanced optimisation systems across domains such as engineering, logistics, and computational design.

关键词： Systematic literature review optimisation algorithms Large language models Swarm intelligence Metaheuristic Generative AI ChatGPT LLM

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Graph theory based topology design and energy routing control of the energy internet

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第20期12卷 4507-4514页

作者： Guo, Hui Wang, Fei James, Geoff Zhang, Lijun Luo, Jian Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Univ Technol Sydney Inst Sustainable Futures POB 123 Ultimo NSW 2007 Australia

As a core of energy internet, the energy router (ER) controlled by information flows can better realise the large scale utilisation of renewable energy. In order to build a cost-effective energy internet, a modified minimum spanning tree algorithm is proposed to optimise the cable layout among ERs, i.e. topology design. Considering the real-time and the asynchrony of power transmission in the above topology determined energy internet, an energy routing control method based on Dijkstra algorithm is put forward for source-and-load pairs to find a no-congestion minimum loss path. Besides, the loss allocation and congestion managements are realised at the same time. Finally, the simulation results prove the feasibility and effectiveness of proposed optimisation algorithms.

关键词： telecommunication network topology Internet trees (mathematics) optimisation telecommunication network routing graph theory modified minimum spanning tree algorithm ER topology determined energy internet energy routing control method no-congestion minimum loss path graph theory based topology design energy router renewable energy cost-effective energy internet Dijkstra algorithm source-and-load pairs loss allocation congestion managements optimisation algorithms

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