A synergic quantum particle swarm optimisation for constrained combinatorial test generation by Xu Guo, Xiaoyu Song, Jian‐tao Zhou

“…Under this scenario, constrained covering array generation (CCAG), a vital combinatorial optimisation issue targeted with constructing a test suite of minimal size while properly addressing constraints, remains challenging in CT. …”
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A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation by Jeridi Ahmed, Moulahi Mohamed Hedi, Khaterchi Hechmi, Zaafouri Abderrahmen

“…While metaheuristic algorithms (MHAs) offer promising solutions, they often face challenges such as slow convergence and difficulty balancing exploration and exploitation. …”
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Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning by Loke Kok Foong, Vojtech Blazek, Lukas Prokop, Stanislav Misak, Farruh Atamurotov, Nima Khalilpoor

“…Future research should explore additional optimisation algorithms and ensemble techniques to improve prediction robustness and accuracy. …”
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HiGMA-DADCN: Hirudinaria granulosa multitropic algorithm optimised double attention enabled deep convolutional neural network for psoriasis classification by Soumya C S, Jayanna H S

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A multi-factorial evolutionary algorithm concerning diversity information for solving the multitasking Robust Influence Maximization Problem on networks by Minghao Chen, Shuai Wang, Jiazhong Zhang

“…To bridge these gaps, this study integrates the multi-tasking optimisation theory into robust influence maximisation, introducing an evolutionary algorithm called DMFEA. …”
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Hydrofoil Installation and Performance Optimisation for Ship Resistance Reduction in Trimaran Through Particle Swarm Optimisation Method by Ghadimi Aliakbar, Ghadimi Parviz, Ghassemi Hassan

“…The optimisation method integrates STAR-CCM+ software with the particle swarm optimisation (PSO) algorithm as the optimiser. …”
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Lightweight Design of a Connecting Rod Using Lattice-Structure Parameter Optimisation: A Test Case for L-PBF by Michele Amicarelli, Michele Trovato, Paolo Cicconi

“…The paper proposes a method to analyse the substitution of bulk volume with optimised lattice structures. The approach considers an early DoE analysis to explore the design space, Finite Element Analysis to evaluate the feasibility of possible design solutions, and Artificial Intelligence tools to look for optimal design solutions, including Genetic Algorithms and Response Surface Methods. …”
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Incremental Reinforcement Learning for Portfolio Optimisation by Refiloe Shabe, Andries Engelbrecht, Kian Anderson

“…This study introduces a recurrent proximal policy optimisation (PPO) algorithm, leveraging recurrent neural networks (RNNs), specifically the long short-term memory network (LSTM) for pattern recognition. …”
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Comparison of Optimisation Techniques for the Electric Vehicle Scheduling Problem by Jacques Wüst, Marthinus Johannes Booysen, James Bekker

“…Despite numerous optimisation approaches proposed in the literature, comparative analyses of these methods remain scarce, with researchers typically focusing on developing novel algorithms rather than evaluating existing algorithms. …”
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Automation of reversible steganographic coding with nonlinear discrete optimisation by Ching-Chun Chang

“…Experimental results validate the near-optimality of the proposed optimisation algorithm when benchmarked against a brute-force method.…”
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Improving Portfolio Management Using Clustering and Particle Swarm Optimisation by Vivek Bulani, Marija Bezbradica, Martin Crane

“…Portfolio management, a critical application of financial market analysis, involves optimising asset allocation to maximise returns while minimising risk. …”
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Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm by Raji Krishna, Hemamalini S

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Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation by Daniel von Eschwege, Andries Engelbrecht

“…Self-adaptive particle swarm optimisation (SAPSO) algorithms aim to adaptively adjust CPs during the optimisation process to improve performance, ideally while reducing the number of performance-sensitive parameters. …”
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Microscopic stress-constrained two-scale topology optimisation for additive manufacturing by Xiaopeng Zhang, Zheng Ni, Akihiro Takezawa, Yaguang Wang, Zhan Kang

“…In this study, a two-scale topology optimisation method with microscopic stress constraints is proposed to find a two-scale structure with maximised stiffness while making the microscopic stress satisfy specified strength criteria. …”
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Design and Maintenance Optimisation of Substation Automation Systems: A Multiobjectivisation Approach Exploration by Andrés Cacereño, David Greiner, Andrés Zuñiga, Blas J. Galván

“…Multiobjective evolutionary algorithms are combined with discrete event simulation while the performance of two state-of-the-art multiobjective evolutionary algorithms is studied. …”
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Trajectory privacy data publishing scheme based on local optimisation and R-tree by Peiqian Liu, Duoduo Wu, Zihao Shen, Hui Wang

“…Therefore, this paper presents a trajectory privacy data publishing scheme, denoted as LORDP, which is based on local optimisation and R-tree. The proposed scheme aims to handle sensitive data while improves trajectory protection effectiveness. …”
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Optimisation of Electric Vehicle Charging Stations Planning Based on Macro and Micro Perspectives by Qiuxuan WANG, Kunxiang DENG, Jiangnan YAN, Jun LI

“…A capacitated maximal service location model (CMSLM) is proposed to optimise the spatial layout of public charging stations by maximizing their ESCD while considering investment budget and charging station capacity limits. …”
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Efficient Positive Semidefinite Matrix Approximation by Iterative Optimisations and Gradient Descent Method by Vali Asimit, Runshi Wang, Feng Zhou, Rui Zhu

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A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction by Jordan J. Bird, Diego R. Faria, Luis J. Manso, Anikó Ekárt, Christopher D. Buckingham

“…The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. …”
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Intelligent Modelling Techniques for Enhanced Thermal Comfort and Energy Optimisation in Residential Buildings by Shamaila Iram, Hafiz Muhammad Athar Farid, Abduljelil Adeola Akande, Hafiz Muhammad Shakeel

“…The ASHRAE Global Thermal Comfort Database II was employed to construct and evaluate machine learning models that were designed to predict thermal comfort levels while optimising energy consumption. Air temperature, garment insulation, metabolic rate, air velocity, and humidity were identified as critical comfort determinants. …”
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