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检索条件"主题词=Optimization algorithms"

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Exploring the Progressive optimization Technique within Response Surface Methodology 4

Exploring the Progressive Optimization Technique within Resp...

引用

4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Yanchun, Jin Bing, Guan Huizhou University School of Geography and Tourism School of Computer Science and Engineering Huizhou China

ISBN: (纸本)9798350363586

Response surface methodology(RSM) is a commonly used statistical method for studying and optimizing the influencing factors in the production process. By establishing mathematical models to describe the relationship between factors and responses, response surface methodology can help us identify and optimize the settings of influencing factors to achieve optimal production results. The results can be demonstrated by graphing in a three-dimensional space. An example involving tourists satisfaction is introduced to this research, which involves how to use this approach and explains the results. In this study, response surface methodology is applied to explore and optimize the key influencing factors in the production and service process using Python. By collecting experimental data and establishing appropriate mathematical models, we assess the impact of various factors on the production and service process and achieve optimal results by optimizing factor levels. Additionally, we conduct further analysis on interaction effects to gain a deeper understanding of how the interactions between influencing factors affect production and service outcomes. © 2024 IEEE.

关键词： optimization algorithms

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Prospect Theory-Based Formulation of Portfolio optimization Problem using Loan And Its Solution by Evolutionary Algorithm 13

Prospect Theory-Based Formulation of Portfolio Optimization ...

引用

Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Tagawa, Kiyoharu Orito, Yukiko Faculty of Informatics Kindai University Dept. of Informatics Higashi-Osaka Osaka Japan College of Engineering Tamagawa University Dept. of Management Science Machida Tokyo Japan

ISBN: (纸本)9798350373332

Portfolio optimization that allows the borrowed money from a loan to be invested in risk assets is formulated as a data-driven optimization problem called POL_P. Then, in order to reflect investor preferences called 'values' in the solution, or the portfolio, prospect theory is used to extend POL_P to another optimization problem called POL_ V. POL_P maximizes the profit of the portfolio, while POL_ V maximizes the value of the portfolio. An evolutionary algorithm with a novel constraint-handling technique is proposed for solving POL_P and POL_ V. Finally, the solutions to POL_P and POL_ V are compared and discussed through experiments using historical asset data. © 2024 IEEE.

关键词： optimization algorithms

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Three-Dimensional Path Planning of Unmanned Aerial Vehicle Based on Hybrid Strategy and Improved Dung Beetle optimization Algorithm 4

Three-Dimensional Path Planning of Unmanned Aerial Vehicle B...

引用

4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Yang, Chenming Dong, Xuezhen Dalian Polytechnic University Dalian China

ISBN: (纸本)9798331528850

As an effective path exploration technology, dung Beetle algorithm has been widely used in the path planning of unmanned aerial vehicle autonomous navigation. In this paper, a 3D path planning strategy for unmanned aerial vehicles (UAS) is proposed by optimizing the dung beetle algorithm, aiming to overcome the challenges of local optimal traps, redundant turning points and low convergence efficiency encountered by traditional intelligent bionic algorithms when dealing with complex spatial path planning of UAS. First, based on mathematical modeling technology, we built a three-dimensional simulation environment of mountain terrain, and precisely set the objective function and constraint conditions of the UAV to ensure the authenticity and practicability of the experiment scene. We innovatively introduced Fuch and Logistic chaotic mapping mechanisms to optimize the initial population allocation of the dung beetle algorithm, which significantly enhanced the global exploration capability of the algorithm. At the same time, we designed an adaptive nonlinear decline strategy to dynamically regulate the number of dung beetles in different stages of the algorithm, thus accelerating the convergence process of the algorithm. In addition, in order to achieve smooth transition of the path, we use Bessel curve technology to perform fine processing on the generated path. © 2024 IEEE.

关键词： optimization algorithms

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A Mutation Lion Swarm optimization Algorithm Based on Proportional Strategy 24

A Mutation Lion Swarm Optimization Algorithm Based on Propor...

引用

3rd Asia Conference on algorithms, Computing and Machine Learning, CACML 2024

作者： Zhao, Ze Jiang, Mingyan Yuan, Dongfeng Jiang, Keqin Wang, Feng Zhou, Xiaotian School of Information Science and Engineering Shandong University China Qilu Institute of Technology China

ISBN: (纸本)9798400716416

During the iterative process, the probability of selection is directly linked to the fitness magnitude, as each iteration of swarm intelligence optimization algorithms progressively converges towards an optimal solution. Building upon this premise, we introduce the Lion Swarm optimization Algorithm based on the Proportional Strategy (PLSO), designed to enhance convergence speed and achieve superior optima. Inspired by the roulette strategy, our approach integrates the concept into the Lion Swarm optimization (LSO) algorithm. In essence, it mimics the behavior of lion cubs, who learn hunting by following lionesses;however, their choice of lioness to follow is governed by rationality, favoring those with superior hunting skills. This fosters knowledge transfer among lionesses, strengthening the link between global and local optima, thereby enhancing local search capabilities and significantly accelerating convergence. In this paper, we evaluate the performance of the PLSO algorithm against six single-peak test functions, four multi-peak test functions, and four additional functions, chosen randomly. Comparative analyses are conducted with classical optimization algorithms, and the PLSO algorithm is applied to address the image segmentation problem. Our findings demonstrate the superior efficacy and robustness of the PLSO algorithm. © 2024 ACM.

关键词： optimization algorithms

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Data Clustering Using Social Group optimization Algorithm 1

引用

8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Nagesh Meesala, Y.V. Parida, Ajaya Kumar School of Computer Engineering KIIT Deemed to be University Bhubaneswar India CSE Department Raghu Engineering College Visakhapatnam India

ISBN: (数字)9789819786664

ISBN: (纸本)9789819786657

A newly developed optimization technique, known as "Social Group optimization (SGO)," has been recently introduced by S.C. Satapathy and colleagues. This innovative method presents an effective strategy for addressing global optimization problems. The paper also puts forth a novel approach to cluster data utilizing the SGO method. It demonstrates that SGO can effectively determine the centroids for a user-defined number of clusters, much like other conventional clustering techniques. To assess its performance, the SGO algorithms are evaluated using various datasets and are compared against established clustering methods, including K-means (KMs), Particle Swarm optimization (PSO) clustering, Differential Evolution (DE) clustering, and Teaching Learning-Based optimization (TLBO) clustering. The results of these evaluations suggest that the SGO clustering technique holds substantial promise and may outperform other existing techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： optimization algorithms

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Beta Random Restart Strategy-Based Remora optimization Algorithm for Global optimization 14

Beta Random Restart Strategy-Based Remora Optimization Algor...

引用

14th International Conference on Information Science and Technology, ICIST 2024

作者： Ai, Zekai Shi, Xiaoming Jia, Heming Yang, Jie Xue, Bowen Du, Yilong College of Design and Engineering National University of Singapore Singapore School of Information Engineering Sanming University Sanming China School of Electrical and Information Engineering Northeast Petroleum University Daqing China College of Information and Electrical Engineering Heilongjiang Bayi Agricultural University Daqing China

ISBN: (纸本)9798350353334

The Remora optimization Algorithm (ROA) is a meta-heuristic algorithm that imitates the foraging behaviors of Remora. Its main idea lies in simulating the mechanism of switching hosts during the foraging process of remora. Due to the randomness of remora host selection, ROA frequently gets trapped in local optima, which slows down its convergence speed. To develop a more robust algorithm, this paper simulates the exploration and elimination mechanism in the biological evolution process and improves the random restart strategy with 'prior' properties. Beta Random Restart Strategy-based Remora optimization Algorithm (BROA) is proposed to realize global optimization. The proposed Beta-random restart strategy ranks individuals by fitness and restarts the less fit half. The Cauchy strategy is used to expand the search area and enhance the ability to escape from local optima. This paper meticulously assesses the performance of BROA using five comparison algorithms. Firstly, the optimization capabilities of BROA are assessed through CEC2020 tests. The Wilcoxon test assesses the difference between BROA and five different algorithms. Finally, Cantilever Beam Design problem is used for testing the practicability of BROA. Comprehensive results show that BROA performs best in CEC2020 and the cantilever beam design problem compared with five different optimization algorithms. © 2024 IEEE.

关键词： optimization algorithms

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Enhancing Student Performance Prediction with Greylag Goose optimization Algorithm

Enhancing Student Performance Prediction with Greylag Goose ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Rizk, Faris H. Mohamed, Mahmoud Elshabrawy Sameh, Basant Zaki, Ahmed Mohamed Eid, Marwa M. El-Kenawy, El-Sayed M. Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt

ISBN: (纸本)9798350351408

The current paper addresses the central role of hyperparameter optimization in improving the predictive power of the MLP Regressor for forecasting student performance in Portuguese secondary schools. The uniqueness of this research lies in its exploration of metaheuristic optimization algorithms, specifically highlighting GGO (Greylag Goose optimization) for enhancement. The study utilized a dataset crucial for understanding and predicting student performance, with a special focus on its distinctive features. By comprehensively tuning the MLP Regressor, the paper demonstrates remarkable improvements in various performance measures, as evident in the enclosed tables. Specifically, the MSE values calculated for the MLP Regressor both before and after GGO optimization are compared. Without optimization, the MLP Regressor had an MSE of 0.0103. After GGO optimization, the MSE significantly improved to 0.0060, indicating enhanced accuracy with GGO in the model. These findings emphasize that hyperparameter optimization, particularly the GGO technique, is crucial for refining the MLP Regressor in predicting student performance. The paper not only delves into the technical aspects but also concludes by highlighting the broader consequences of these outcomes. The potential educational applications are substantial, as improved models can provide more accurate predictions, empowering educators and policymakers to make informed decisions in education. This paper establishes a foundation for future research directions, contributing to the pool of ideas for educational predictive modeling. © 2024 IEEE.

关键词： optimization algorithms

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Principled Preferential Bayesian optimization 41

Principled Preferential Bayesian Optimization

引用

41st International Conference on Machine Learning, ICML 2024

作者： Xu, Wenjie Wang, Wenbin Jiang, Yuning Svetozarevic, Bratislav Jones, Colin N. Automatic Control Laboratory EPFL Lausanne Switzerland Urban Energy Systems Laboratory Empa Zurich Switzerland The Institute for Artificial Intelligence Research and Development of Serbia Serbia

We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions. Inspired by the likelihood ratio idea, we construct a confidence set of the black-box function using only the preference feedback. An optimistic algorithm with an efficient computational method is then developed to solve the problem, which enjoys an information-theoretic bound on the total cumulative regret, a first-of-its-kind for preferential BO. This bound further allows us to design a scheme to report an estimated best solution, with a guaranteed convergence rate. Experimental results on sampled instances from Gaussian processes, standard test functions, and a thermal comfort optimization problem all show that our method stably achieves better or competitive performance as compared to the existing state-of-the-art heuristics, which, however, do not have theoretical guarantees on regret bounds or convergence. Copyright 2024 by the author(s)

关键词： optimization algorithms

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Multi sensor and multi task allocation based on improved whale optimization algorithm 34th

Multi sensor and multi task allocation based on improved wha...

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34th Congress of the International Council of the Aeronautical Sciences, ICAS 2024

作者： Yuano, Binlin Zhang, Shifeng Wang, Boxuan Zhang, Kun College of Aerospace Science and Engineering National University of Defense Technology Changsha China Sichuan Tengden Technology Co. Ltd Chengdu China School of Electronic and Information Northwestern Polytechnical University Xi’an China

In response to the collaborative detection problem of unmanned aerial vehicle (UAV) formations in dual aircraft formation cluster operations, this paper constructs an optimization model with detection benefits and detection costs as the objectives based on task priority, and designs an improved whale optimization algorithm to solve the model. Firstly, an elite population initialization based on multiple constraint conditions was proposed, and secondly, an improved method for updating whale individual positions was proposed by combining model characteristics. Subsequently, a local search strategy was designed to enable the algorithm to escape from local optima. Finally, the effectiveness of the proposed algorithm in solving task allocation problems was verified through simulation experiments. Compared with the Harris Eagle algorithm, the improved whale optimization algorithm obtains a higher quality non dominated solution set, indicating that the algorithm has certain advantages. © 2024, International Council of the Aeronautical Sciences. All rights reserved.

关键词： optimization algorithms

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optimization of WSNs Coverage for Power Monitoring Based on Multi-strategy Sand Cat Swarm optimization Algorithm 12

Optimization of WSNs Coverage for Power Monitoring Based on ...

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12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： Fei, Hongmei Luo, Tao Zhang, Baitao Sun, Zheng Ma, Pengwei Zhou, Jie College of Information Science and Technology Shihezi University Shihezi China

ISBN: (纸本)9798350355802

With the rapid development of electric power technology, the application of wireless sensor networks (WSNs) in the electric power field has received widespread attention. The WSNs coverage optimization problem in the power field is one of the key factors to improve the power transmission efficiency and resource utilization. However, traditional optimization algorithms have some limitations in dealing with the deployment problem of large-scale sensor nodes. Therefore, this thesis proposes a multi-strategy sand cat swarm optimization algorithm (MSSCSO) to solve the WSNs coverage problem. First, this paper proposes an improved Tent chaos mapping to improve the search range and enhance the local search capability of the algorithm. Secondly, the reverse learning strategy is used to increase the population diversity and global search capability, which improves the convergence speed and optimization accuracy of the algorithm. Finally, the global search capability of the algorithm and the adaptability and robustness of the algorithm are enhanced by the adaptive strategy. In order to evaluate the performance of the proposed method, this paper conducts comparative experiments with Genetic Algorithm (GA), Firefly optimization Algorithm (FA), Whale optimization Algorithm (WOA) and Gray Wolf optimization Algorithm (GWO), respectively. The experimental results show that MSSCSO's WSNs coverage optimization method can effectively improve the network coverage in power scenarios by 10.54%, 20.04%, 9.81%, and 6.43% over GA, FA, WOA, and GWO, respectively. Meanwhile, the method has strong adaptability and stability to the uncertainty and dynamic changes in the power environment. © 2024 IEEE.

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