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6th Social Robots with AI: Prospects, Risks, and Responsible Methods Robophilosophy, RP 2024

作者： Stenseke, Jakob Department of Philosophy Lund University Sweden

ISBN: (纸本)9781643685670

Can AI 'solve' politics? In this paper, I explore optipolitics, i.e., the idea that politics along with other complex social issues can be framed as mathematical optimization problems and solved as such. I begin by describing politics, optimization, and some reasons for applying the latter to the former. I then present a liberal democratic version of optipolitics and try to defend it against eight fundamental challenges. While I concede that none of the challenges can be satisfactorily overcome at present, many can be shown to be at least as difficult to overcome in conventional representative democracy. Most importantly, as a logical endpoint of AI-driven technocracy, I argue that optipolitics can serve as a useful starting-point for a critical debate about the future of democratic governance in the age of machines. © 2025 IOS Press. All rights reserved.

关键词： optimization algorithms

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How the choice of model calibration procedure affects projections of lake surface water temperatures for future climatic conditions

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JOURNAL OF HYDROLOGY 2025年 659卷

作者： Napiorkowski, Jaroslaw J. Piotrowski, Adam P. Osuch, Marzena Zhu, Senlin Karamuz, Emilia Polish Acad Sci Inst Geophys Ks Janusza 64 PL-01452 Warsaw Poland Yangzhou Univ Coll Hydraul Sci & Engn Yangzhou 225127 Peoples R China

Almost all hydrological models require calibration. The same model but with different parameters may lead to diverse simulations of the hydrological phenomena. Hence, the choice of a calibration method may affect the model performance. The present paper is the first study analyzing how the choice of air2water model calibration procedure may influence projections of surface water temperature in lowland lakes under future climatic conditions. To address this issue, projections from 14 atmospheric circulation models, data from 22 lowland Polish lakes located in a temperate climate zone, and 12 different optimization algorithms are employed. The studied lake areas range from 1.5 km2 to 115 km2, and their maximum depths range from 2.5 m to 70 m. Depending on which calibration algorithm is applied, the differences in mean monthly surface water temperatures projected for future climatic conditions may exceed 1.5 degrees C for a small deep lake. On the contrary, the differences observed for shallow and relatively large lakes, due to the optimization procedure used, were lower than 0.6 degrees C each month. The largest differences in projected lake water temperatures were observed for the winter and summer months, which are especially critical for aquatic biota. Among the optimization algorithms resulting in the largest differences were those that fit historical data well, as well as those that do not reproduce historical data appropriately. Therefore, strong performance for historical data does not guarantee reliable projections for future conditions. We have shown that projected lake water temperatures largely depend on the calibration method used for a particular model.

关键词： Surface lake water temperature Air2water Climate change optimization algorithms Model reliability Impact studies Hydrological projections

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A Review of Enhancing Sine Cosine Algorithm: Common Approaches for Improved Metaheuristic algorithms

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2024年 1-58页

作者： Hamad, Qusay Shihab Saleh, Sami Abdulla Mohsen Suandi, Shahrel Azmin Samma, Hussein Hamad, Yasameen Shihab Hussien, Abdelazim G. Univ Informat Technol & Commun UOITC Baghdad Iraq Univ Sains Malaysia Sch Elect & Elect Engn Intelligent Biometr Grp Engn Campus Nibong Tebal 14300 Penang Malaysia King Fahd Univ Petr & Minerals SDAIA KFUPM Joint Res Ctr Artificial Intelligence Dhahran Saudi Arabia Minist Educ Baghdad Iraq Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt

In recent years, the quest for optimizing metaheuristic algorithms has led to a surge in research efforts aimed at enhancing their performance. While existing reviews have diligently summarized these endeavors, they primarily focus on presenting the collective body of work undertaken to augment standard algorithms. In contrast, this paper takes a unique perspective by concentrating on the myriad methodologies employed by authors to improve one such algorithm, the Sine Cosine Algorithm (SCA). Our comprehensive review dissects the various strategies used to elevate the effectiveness of SCA variants, meticulously scrutinizing their advantages and disadvantages. This in-depth analysis extends beyond the confines of SCA and provides valuable insights into the broader landscape of metaheuristic optimization algorithms. By evaluating the pros and cons of these enhancement methods, our work forms a foundational review that can be applied to other optimization algorithms. Through this broader lens, we offer readers a comprehensive overview of the strategies adopted by researchers in recent years to enhance optimization algorithms, facilitating a deeper understanding of the advancement of this vital field. Our paper thus serves as a guidepost for researchers and practitioners navigating the ever-evolving terrain of metaheuristic optimization, shedding light on the strengths and potential pitfalls of enhancement methodologies. It provides a holistic perspective that empowers the community to make informed choices when selecting or devising strategies to optimize algorithms for diverse problem domains.

关键词： optimization algorithms

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Estimating the compressive and tensile strength of basalt fibre reinforced concrete using advanced hybrid machine learning models

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STRUCTURES 2025年 71卷

作者： Ullah, Irfan Javed, Muhammad Faisal Alabduljabbar, Hisham Ullah, Hafeez Hohai Univ Dept Civil & Transportat Engn Nanjing 210098 Peoples R China GIK Inst Engn Sci & Technol Dept Civil Engn Swabi 23640 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan

Basalt fibre has recently become a popular choice for concrete reinforcement due to its superior mechanical properties and sustainable production process. This research presents novel hybrid machine learning models for predicting the compressive strength (CS) and tensile strength (TS) of basalt fibre reinforced concrete (BFRC). The study integrates support vector regression (SVR) with firefly algorithm (FFA), grey wolf optimization (GWO), and particle swarm optimization (PSO) to develop hybrid models for forecasting BFRC properties. Random forest (RF) and decision tree (DT) were also employed for comparison. SVR-PSO exhibited the strongest performance, achieving the highest coefficient of determination (R2) scores of 0.993 for CS and 0.954 for TS, surpassing SVRFFA (CS = 0.990, TS = 0.944) and SVR-GWO (CS = 0.977, TS = 0.930). The RF model achieved R2 values of 0.974 for CS and 0.918 for TS, while the DT model had R2 values of 0.865 for CS and 0.897 for TS. SHapley Additive exPlanations (SHAP) analysis revealed the water-to-cement ratio (W/C) as the most critical feature for CS, while fine aggregate (FA) was most significant for TS. Partial dependence plots (PDP) analysis indicated FC and FA negatively affect CS, whereas FC and CA positively influence TS. A user-friendly graphical user interface was developed to streamline the prediction of CS and TS, crucial for ensuring the safety and stability of buildings and bridges. Future research should consider incorporating additional input features to enhance the accuracy of CS and TS predictions for BFRC. Expanding datasets is essential for the effective implementation of deep learning algorithms. The proposed hybrid models demonstrated high efficacy in predicting CS and TS, suggesting their potential application in estimating the durability characteristics of BFRC.

关键词： Basalt fibres Machine learning optimization algorithms

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A Neurodynamic Approach for Distributed optimization with Delayed Feedback 13

A Neurodynamic Approach for Distributed Optimization with De...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Qi, Yucheng Wang, Mengxin Jiang, Xinrui Qin, Sitian Weihai264209 China

ISBN: (纸本)9798331516147

In this paper, a neurodynamic approach with communication delay is proposed for the sake of solving distributed optimization problems. First, the relationship between the equilibrium of the algorithm and the optimal solution of the distributed optimization problem is established. Then, sufficient conditions for the equilibrium to converge to the optimal solution of the optimization problem are provided in the form of linear matrix inequalities, in the case of communication delay. In addition, numerical simulation verifies the validity of the neurodynamic approach with communication time delay. © 2025 IEEE.

关键词： optimization algorithms

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Adaptive Normal-Boundary Intersection Directions for Evolutionary Many-Objective optimization with Complex Pareto Fronts 13th

Adaptive Normal-Boundary Intersection Directions for Evolut...

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13th International Conference on Evolutionary Multi-Criterion optimization, EMO 2025

作者： Elarbi, Maha Bechikh, Slim Coello Coello, Carlos A. SMART Lab ISG University of Tunis Tunis Tunisia Departamento de Computacion CINVESTAV-IPN Mexico City Mexico

ISBN: (纸本)9789819635054

Decomposition-based Many-Objective Evolutionary algorithms (MaOEAs) usually adopt a set of pre-defined distributed weight vectors to guide the solutions towards the Pareto optimal Front (PF). However, when solving Many-objective optimization Problems (MaOPs) with complex PFs, the effectiveness of MaOEAs with a fixed set of weight vectors may deteriorate which will lead to an imbalance between convergence and diversity of the solution set. To address this issue, we propose here an Adaptive Normal-Boundary Intersection Directions Decomposition-based Evolutionary Algorithm (ANBID-DEA), which adaptively updates the Normal-Boundary Intersection (NBI) directions used in MP-DEA. In our work, we assist the selection mechanism by progressively adjusting the NBI directions according to the distribution of the population to uniformly cover all the parts of the complex PFs (i.e., those that are disconnected, strongly convex, degenerate, etc.). Our proposed ANBID-DEA is compared with respect to five state-of-the-art MaOEAs on a variety of unconstrained benchmark problems with up to 15 objectives. Our results indicate that ANBID-DEA has a competitive performance on most of the considered MaOPs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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optimization Algorithm Based Path Planning and Speed Improvement Problems 2

Optimization Algorithm Based Path Planning and Speed Improve...

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2nd IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Liao, Bingquan He, Peiqi Zhao, Yaqin Yin, Luxiu Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798350368208

This paper simulates and optimises the movement process of the traditional folk cultural activity 'bench dragon' by means of a mathematical model to optimise its path and speed. Firstly, a pitch optimisation model is constructed in this paper. The constraint is that no collision occurs between the dragon handle and the bench in the process of reaching the boundary of the turnaround space. A bifurcated search algorithm is used to simulate by continuously adjusting the pitch, and the minimum pitch is finally obtained. Then, this paper constructs an optimisation model for solving the minimisation of the length of the turnaround curve. We need to determine the disc entry point and select the easily available disc exit point on the screw line that meets the requirements, use the geometric relationship to establish a system of equations about the quadratic equation, and use the solve function in MATLAB to solve the problem, and finally get the shortest arc length. Finally, a dynamic speed optimisation model is developed in this paper. Taking maximising the travelling speed of the faucet as the optimisation objective, keeping the travelling speed of the faucet unchanged, and choosing the dichotomous search algorithm, the speed of the faucet is calculated through iteration to get the maximum travelling speed of the faucet that satisfies all the constraints. © 2024 IEEE.

关键词： optimization algorithms

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An Improved Secretary Bird optimization Algorithm 5

An Improved Secretary Bird Optimization Algorithm

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5th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

作者： Qin, Hongwu Yang, Songhao Liu, Zhenqi Li, Guangxi Changchun University School of Electronic Information Engineering Changchun China

ISBN: (纸本)9798350352719

The traditional secretary bird optimization algorithm is known to exhibit certain deficiencies, including a slow convergence speed and a tendency to fall into local optimization. This paper presents an enhanced version of the algorithm, addressing these limitations. The proposed algorithm incorporates three key innovations. First, it employs Circle chaotic mapping to generate an initialized population with greater biodiversity. Second, it integrates a differential evolution strategy into the iterative process, balancing global search and local optimization to improve convergence accuracy. Additionally, a population diversity strategy is introduced to enhance convergence speed and optimization accuracy. This strategy was tested on six benchmark functions and compared with other optimization algorithms. Experimental results demonstrate that ISBOA achieves optimal convergence speed and optimization accuracy, validating its effectiveness on complex problems. © 2024 IEEE.

关键词： optimization algorithms

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optimization Algorithm of Multi-Attribute Group Decision-Making Based on Information Entropy Theory 4

Optimization Algorithm of Multi-Attribute Group Decision-Mak...

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4th International Conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Feng, Xuan Cai, Youbao Institute of Computer Science Sichuan Province Chengdu China

ISBN: (纸本)9798350380323

The problems of complex mathematical models, high computational complexity, and repeated parameter adjustments in the subjective and objective weighting method is studied. Such problems are generally defined as multi-attribute group decision-making models. Multi-attribute group decision-making models have been widely applied in fields such as achievement evaluation, scientific research performance assessment, and project review, and are a popular research direction in the field of artificial intelligence. How to balance the subjectivity among experts and the objectivity of attribute weights is a challenging problem in the research of multi-attribute group decision-making models. The entropy weighting method is firstly used to obtain the objective weights of attributes. Then, the grey correlation degree method is applied to adjust the subjective weight. Finally, based on the minimum Kullback-Leibler divergence method criterion reflecting the distance between different weights, an optimization model is established to solve the integrated weight. It is provided a feasible weight integration method based on information entropy theory for multi-attribute group decision-making. A numerical experiment shows the feasibility and practicability of the method. It is provided a new solution to the problems of prioritizing quantity over quality in the performance evaluation of scientific research in universities and research institutions, which can effectively promote the enthusiasm of scientific and technological workers for scientific research innovation. © 2024 IEEE.

关键词： optimization algorithms

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Numerical Investigation of the Swarm Intelligence Algorithm Obtained Using ChatGPT for Univariate Global optimization 23rd

Numerical Investigation of the Swarm Intelligence Algorithm ...

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23rd International Conference on Mathematical optimization Theory and Operations Research, MOTOR 2024

作者： Sorokovikov, Pavel Gornov, Alexander Zarodnyuk, Tatiana Matrosov Institute for System Dynamics and Control Theory of SB RAS Lermontov Str. 134 Irkutsk664033 Russia

ISBN: (纸本)9783031733642

This paper explores the possibility of designing an efficient global optimization algorithm using an artificial intelligence chatbot, ChatGPT. The main idea is to use the swarm intelligence metaheuristic method, which operates on a set of particles in a swarm, to solve non-local univariate search problems. Testing was carried out on a set of one-dimensional non-convex functions with varying numbers of local optima. The results demonstrate that this algorithm can be used to solve multi-extremal optimization problems. The paper presents the results of a numerical study on the properties of this algorithm in comparison with the Piyavsky method, the Strongin method, and a combination of the Strongin with the Parabolas methods. The performed computational experiments testify to the suitability of the obtained swarm intelligence algorithm for solving univariate non-local search problems. This approach can also be useful for solving auxiliary optimization problems and for developing multimethod computational technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： optimization algorithms

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