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14th International Workshop of Advanced Manufacturing and Automation, IWAMA 2024

作者： Zhang, Linjun Zhou, Chuanhong Chen, Yuan School of Automation and Mechanical Engineering Shanghai University Shanghai200072 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai200072 China

ISBN: (纸本)9789819626243

This paper implements a greedy tabu search algorithm to deal with the offline palletizing problem, and proposes a two-phase greedy tabu search algorithm. In the first phase, a greedy stochastic adaptive search algorithm is utilized to obtain the local optimal solution of the palletizing problem in the construction process and the local search process, which includes the residual space strategy and the combination of the cargo block strategy, as well as the optimization of the space utilization rate by using the tiling wall strategy. In the second stage, the local optimal solution obtained in the first stage is used as the initial solution, and the tabu search algorithm is used to record the history information of the solution through the tabu table to prevent the algorithm from falling into the local optimal solution, so as to dig deeper into the global optimal solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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A Self-adaptive Population Rao Algorithm for the Reservoir Benchmark Problem 28th

A Self-adaptive Population Rao Algorithm for the Reservoir B...

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28th International Conference on Hydraulics, Water Resources, River and Coastal Engineering, HYDRO 2023

作者： Baladaniya, K.B. Patel, P.L. Timbadiya, P.V. Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Surat Surat395007 India

ISBN: (纸本)9789819774739

The technique proposed in this paper, the self-adaptive population hybrid Rao algorithm (SAPHR), is intended to handle single- and multi-objective optimization problems. Unlike traditional methods, it does away with the necessity for metaphors and particular algorithm settings. According to improvements in the fitness value seen during the search process, the algorithm under consideration modifies the population size. The population is divided into three sub-population groups through a random process. Randomly assigned perturbation equations are provided to each sub-population, with each Equation guiding solutions to explore distinct regions within the search space. Evaluating the performance of the proposed algorithm involves using the well-known reservoir benchmark problem known as the discrete four-reservoir operation (DFRO) problem. The results of the algorithm are compared with other methods from the past study, revealing that the proposed method achieves superior outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Optimal Reservoir Operation Using Rao algorithms 28th

Optimal Reservoir Operation Using Rao Algorithms

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28th International Conference on Hydraulics, Water Resources, River and Coastal Engineering, HYDRO 2023

作者： Paliwal, V. Ghare, A.D. Mirajkar, A.B. Moze College of Engineering Pune India V.N.I.T Nagpur India

ISBN: (纸本)9789819774661

In this study, Rao algorithms (metaphor-less heuristic algorithms) are applied to derive the optimal operational releases of the Mula reservoir, Upper Godavari basin. The optimal operation releases for 75% probable inflows are compared to the optimal operational releases obtained from Jaya Algorithm (JA) and Non Linear Programming (NLP). Simulation studies are carried out using real inflows corresponding to 75% probable inflows. The results show that Rao algorithms provided better and comparable results to the result obtained from JA and NLP. Rao algorithms are found to be suitable in application to reservoir operation studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Accelerated Successive Convex Approximation for Nonlinear optimization-Based Control

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IEEE Transactions on Automatic Control 2025年

作者： Wu, Jinxian Dai, Li Dou, Songshi Xia, Yuanqing Beijing Institute of Technology School of Automation Beijing100081 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

The successive convex approximation (SCA) methods stand out as the viable option for nonlinear optimization-based control, as it effectively addresses the challenges posed by nonlinear (potentially non-convex) optimization problems by transforming them into a sequence of strongly convex subproblems. However, the current SCA algorithm exhibits a slow convergence rate, resulting in a relatively poor performance within a limited sample time. In this paper, the process of SCA is retreated as solving a fixed point nonlinear equation. By analyzing the derivative properties of this nonlinear equation, we introduce a Newton-based accelerated SCA (ASCA) algorithm designed to enhance the local convergence rate while inheriting all favorable characteristics of the SCA methods. Specifically, our algorithm offers the following benefits: (i) It is capable of effectively tackling nonlinear optimization-based control problems;(ii) It permits flexible termination with all generated intermediate solutions being feasible for the original nonlinear problem;and (iii) It guarantees convergence with locally superlinear convergence rate to the stationary point of the original nonlinear problem. Finally, we conduct experiments in a multi-agent collision avoidance scenario to show its validity. © 1963-2012 IEEE.

关键词： optimization algorithms

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Dynamic Change of Three-Dimensional Soil in Game Simulators

Dynamic Change of Three-Dimensional Soil in Game Simulators

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International IoT, Electronics and Mechatronics Conference, IEMTRONICS 2024

作者： Belov, Aleksandr Sapozhnikov, Artem Semichasnov, Iliya Departament of the Applied Mathematics National Research University "Higher School of Economics" Moscow Russia

ISBN: (纸本)9789819747832

This article describes the practical usage of mathematical algorithms in a computer game to create realistic soil behavior in contact with external objects. The study uses a set of algorithms to simulate the behavior of soil, specifically contact point detection algorithm, basic deformation algorithm, soil compaction algorithm, displaced mass distribution algorithm, and erosion algorithm. A comparative analysis of several algorithms was conducted, and the most effective algorithms were identified. Based on the results of this analysis, a final simulation was performed to showcase examples of soil types that can be simulated using this approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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A Dynamic Mission Planning Method of Multi-Satellite Cooperative Observation for Highly Time-Sensitive Targets 15

A Dynamic Mission Planning Method of Multi-Satellite Coopera...

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15th Asia Conference on Mechanical and Aerospace Engineering, ACMAE 2024

作者： Jiaxin, Hu Demin, Zhao Yanwei, Zhu College of Aerospace Science National University of Defense Technology Changsha410000 China DFH Satellite Co. Ltd. Beijing10094 China

ISBN: (纸本)9781643685892

To address the challenges posed by highly time-sensitive targets with uncertainty and unpredictability in multi-satellite cooperative observation, conventional intelligent optimization algorithms often suffer from time-consuming and unstable issues. This paper presents a novel dynamic mission planning method that cleverly incorporates waiting time for tracking and tracked time as heuristics to guide task assignment. Furthermore, plannable and robustness indicators are introduced to facilitate global trade-offs in resource conflicts among multiple targets. Initially, the constraints specifically suitable for time-sensitive target observation are analyzed, followed by the modeling of the planning problem. Subsequently, a dynamic mission planning approach is proposed that utilizes dynamic priorities, offering a linear time solution for task assignment. Through simulation verification, the efficacy of the proposed method is demonstrated, showcasing its ability to enhance multi-satellite cooperative observation effectively. Additionally, it maximizes resource utilization in the face of diverse conflicts. © 2025 The Authors.

关键词： optimization algorithms

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A new optimal prediction technique for energy demand based on CNN and improved water strider algorithm: a study on socio-economic-climatic parameters

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EVOLVING SYSTEMS 2022年第6期13卷 759-775页

作者： Liao, Shimin Jimenez, Giorgos Maintenance Branch Co Fujian Elect Power Co Ltd Fuzhou 350000 Peoples R China Univ Wisconsin Madison Dept Engn Madison WI 53706 USA

Rising demand and the potential decline in renewables resources have made energy production one of the most important challenges of the future. In hydropower plants, various factors can be the reasons to limit resources and energy supply. In this study, population and GDP, and climatic parameters (temperature and precipitation) are analyzed for the prediction of the energy demand. The innovation of this research is the Introduction of a developed algorithm called the Improved Water Strider Algorithm (IWSA). The results displayed that the improved algorithm has the faster convergence and the lowest error with the value of 1.55, 1.48, 1.44, and 0.78 in population, and GDF, temperature, and precipitation, respectively. This model has the highest correlation coefficient with the value of 0.77, 0.79, 0.8, and 80 in population, GDF, temperature, and precipitation, respectively. Also, this model with the highest correlation of 0.91, and the lowest error of 0.47 can have the best performance. Therefore, after confirming this proposed method, energy demand is predicted. The results showed that among the four input parameters of the CNN-IWSA model the precipitation parameter can have a significant effect on limiting resources and increasing demand because it can directly affect the amount and timing of energy production distribution. The results also show an increasing trend in energy demand for the next 20 years. These results can be of great help to hydrologists and energy managers in controlling and supplying energy.

关键词： Hydropower The energy demand optimization algorithms Energy supply GDP

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Spatiotemporal assessment of landslide susceptibility in Southern Sichuan, China using SA-DBN, PSO-DBN and SSA-DBN models compared with DBN model

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ADVANCES IN SPACE RESEARCH 2022年第8期69卷 3071-3087页

作者： Li, Jiaying Wang, Weidong Chen, Guangqi Han, Zheng Cent South Univ Sch Civil Engn Changsha 410075 Hunan Peoples R China Cent South Univ MOE Key Lab Engn Struct Heavy Haul Railway Changsha 410075 Hunan Peoples R China Kyushu Univ Dept Civil & Struct Engn Fukuoka 8190395 Japan Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Pro Chengdu 610000 Peoples R China

Landslide is one of the most serious geo-hazards, and the landslide susceptibility assessment (LSA) is an existing effective method to efficiently mitigate the loss caused by landslides. This study develops the improved deep belief network (DBN) models by selecting the optimal model hyperparameter to improve the accuracy of LSA. Evaluation factors of the LSA are selected from fifteen influencing fac-tors by chi-square test, multicollinearity test and out-of-bag error. 30% data of the study area are selected randomly as the training data to assess the landslide susceptibility of each grid in the study area. The spatial LSA is then obtained by integrating the DBN models with three optimization algorithms, namely the simulated annealing (SA), particle swarm optimization (PSO) and sparrow search algorithm (SSA). The assessment results obtained using DBN and improved DBN models are thus compared and verified using the receiver operating characteristic (ROC) curve and seed cell area index. It shows that the three improved DBN models outperform the DBN model, which demonstrates the ability of optimization algorithms to improve model performance, and the SSA-DBN model achieves the highest assessment accuracy, followed by the PSO-DBN and SA-DBN models. Meanwhile, the effective rainfall model and peak ground acceleration are respectively employed to evaluate the impact of two inducing factors, namely the rainfall and earthquake, and the temporal LSA is thus obtained. The spatiotemporal LSA map is then generated by coupling the optimal spatial LSA map and temporal LSA map. Therefore, the present study further explores the proposed improved methods and offers instructions for spatiotemporal LSA. (c) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： DBN model optimization algorithms SA-DBN PSO-DBN SSA-DBN Spatiotemporal LSA

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EAOA: An Enhanced Archimedes optimization Algorithm for Feature Selection in Classification

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IEEE ACCESS 2021年 9卷 120795-120814页

作者： Desuky, Abeer S. Hussain, Sadiq Kausar, Samina Islam, Md Akhtarul El Bakrawy, Lamiaa M. Al Azhar Univ Fac Sci Cairo 11754 Egypt Dibrugarh Univ Examinat Branch Dibrugarh 786004 Assam India Univ Kotli Azad Jammu & Kashmir Dept Comp Sci & IT Kotli Azad Kashmir 11100 Pakistan Khulna Univ Stat Discipline Sci Engn & Technol Sch Khulna 9208 Bangladesh

Feature selection plays a crucial role in order to mitigate the high dimensional feature space in different classification problems. The computational cost is reduced, and the accuracy of the classification is improved by reducing the dimension of feature space. Hence, in the classification task, finding the optimal subset of features is of utmost importance. Metaheuristic techniques have proved their efficacy in solving many real-world optimization issues. One of the recently introduced physics-inspired optimization methods is Archimedes optimization Algorithm (AOA). This paper proposes an Enhanced Archimedes optimization Algorithm (EAOA) by adding a new parameter that depends on the step length of each individual while revising the individual location. The EAOA algorithm is proposed to improve the AOA exploration and exploitation balance and enhance the classification performance for the feature selection issue in real-world data sets. Experiments were performed on twenty-three standard benchmark functions and sixteen real-world data sets to investigate the performance of the proposed EAOA algorithm. The experimental results based on the standard benchmark functions show that the EAOA algorithm provides very competitive results compared to the basic AOA algorithm and five well-known optimization algorithms in terms of improved exploitation, exploration, local optima avoidance, and convergence rate. In addition, the results based on sixteen real-world data sets ascertain that reduced feature subset yields higher classification performance when compared with the other feature selection methods.

关键词： Archimedes optimization algorithm classification feature selection machine learning metaheuristic algorithms optimization algorithms

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Precise modeling of PEM fuel cell using improved chaotic MayFly optimization algorithm

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第13期45卷 18754-18769页

作者： Shaheen, Mohamed A. M. Hasanien, Hany M. El Moursi, M. S. El-Fergany, Attia A. Future Univ Egypt Elect Engn Dept Cairo 11835 Egypt Ain Shams Univ Elect Power & Machines Dept Fac Engn Cairo Egypt Khalifa Univ Elect Engn & Comp Sci Dept Abu Dhabi U Arab Emirates Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig Egypt

The accuracy of the modeling of the fuel cell is important for achieving precise simulation results. This article presents a newly developed optimization method "Chaotic MayFly optimization algorithm" (CMOA) for obtaining the proton exchange membrane fuel cell (PEMFC) parameters. This research mainly targets an accurate modeling of the PEMFC that provides good match between the simulation results and those measured practically. In this regard, the I-V characteristics of the PEMFC's are non-linear, and there are seven design variables are considered because of the manufacturer's shortage in providing such information. The optimization problem formulated in this study is a non-linear problem. The objective function is mathematically expressed as the total squared error between the PEMFC terminal voltage measured in the laboratory vs the estimated terminal voltage from the simulation of the model. Since the metaheuristic optimization techniques are significantly influenced by the problem initialization, a new hybridization between the chaotic mapping and the MOA is employed to tackle the problem of the PEMFC design variables estimation and achieving better results. The CMOA is applied to find the best solution of the objective function that satisfies the preset conditions. The accurateness of the PEMFC approximated model is verified numerically using the optimal design variables. The simulation results are verified under various conditions of temperature and pressure. The estimated numerical results are compared with the measured data in case of many standard PEMFCs, such as Ballard, Mark V 5 kW, 500 W BCS, and 250 W stacks. The robustness of the proposed CMOA applied to the PEMFC model is also tested. The findings of the simulations of the proposed CMOA are compared with other findings obtained by other optimization methods. Applying the CMOA results in an accurate development of the PEMFC model.

关键词： fuel cell modeling optimization algorithms parameter determination polarization curves

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