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41st International Conference on Machine Learning, ICML 2024

作者： Provodin, Danil Kaptein, Maurits Pechenizkiy, Mykola Eindhoven University of Technology Eindhoven Netherlands Jheronimus Academy of Data Science 's-Hertogenbosch Netherlands University of Jyväskylä Jyväskylä Finland

We present a new algorithm based on posterior sampling for learning in Constrained Markov Decision Processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achieves near-optimal regret bounds while being advantageous empirically compared to the existing algorithms. Our main theoretical result is a Bayesian regret bound for each cost component of Õ(DS√AT) for any communicating CMDP with S states, A actions, and diameter D. This regret bound matches the lower bound in order of time horizon T and is the best-known regret bound for communicating CMDPs achieved by a computationally tractable algorithm. Empirical results show that our posterior sampling algorithm outperforms the existing algorithms for constrained reinforcement learning. Copyright 2024 by the author(s)

关键词： optimization algorithms

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Binary Coati optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Predicting Reservoir Water Level

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WATER 2023年第8期15卷 1593页

作者： Sammen, Saad Sh. Ehteram, Mohammad Khozani, Zohreh Sheikh Sidek, Lariyah Mohd Univ Diyala Coll Engn Dept Civil Engn Baqubah 32001 Iraq Semnan Univ Dept Water Engn Semnan *** Iran Bauhaus Univ Weimar Inst Struct Mech Fac Civil Engn D-99423 Weimar Germany Univ Tenaga Nas Inst Energy Infrastruct IEI Coll Engn Dept Civil Engn Kajang 43000 Malaysia

Predicting reservoir water levels helps manage droughts and floods. Predicting reservoir water level is complex because it depends on factors such as climate parameters and human intervention. Therefore, predicting water level needs robust models. Our study introduces a new model for predicting reservoir water levels. An extreme learning machine, the multi-kernel least square support vector machine model (MKLSSVM), is developed to predict the water level of a reservoir in Malaysia. The study also introduces a novel optimization algorithm for selecting inputs. While the LSSVM model may not capture nonlinear components of the time series data, the extreme learning machine (ELM) model-MKLSSVM model can capture nonlinear and linear components of the time series data. A coati optimization algorithm is introduced to select input scenarios. The MKLSSVM model takes advantage of multiple kernel functions. The extreme learning machine model-multi-kernel least square support vector machine model also takes the benefit of both the ELM model and MKLSSVM model models to predict water levels. This paper's novelty includes introducing a new method for selecting inputs and developing a new model for predicting water levels. For water level prediction, lagged rainfall and water level are used. In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. The testing means absolute of the same models was 0.710, 0.742, 0.832, 0.871, 0.912, and 0.919, respectively. The Nash-Sutcliff efficiency (NSE) testing of the same models was 0.97, 0.94, 0.90, 0.87, 0.83, and 0.18, respectively. The ELM-MKLSSVM model is a robust tool for predicting reservoir water levels.

关键词： hybrid models hydrological simulations optimization algorithms water level

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optimization-based damage detection in composite structures using incomplete measurements

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STRUCTURES 2023年 56卷

作者： Hassani, Sahar Dackermann, Ulrike Univ New South Wales Ctr Infrastruct Engn & Safety Sch Civil & Environm Engn Sydney NSW Australia

Incomplete data poses challenges in accurately assessing structural health and detecting damage. It limits the ability to capture the complete behavior and response of the structure, which may hinder the identification and localization of potential damage or anomalies. Addressing the issue of incomplete data requires developing strategies and algorithms that can effectively handle missing or limited measurements. Using incomplete and noisy measurements, we propose an optimization-based damage detection method for laminated composite plates with closely-spaced eigenvalues. The proposed method consists of two stages. In the first stage, the most probable defective elements are identified by utilizing condensed mode shapes as incomplete noisy inputs for modal residual vectors. This approach significantly reduces the computational effort for damage estimation. The second stage introduces an objective function based on incomplete and noisy Condensed Frequency Response Functions (CFRFs). To optimize the damage quantification, the Improved Particle Swarm optimization (IPSO) algorithm is employed to minimize errors in the proposed objective function based on CFRFs of damaged and intact laminates. The proposed method is demonstrated on laminated composite plates with different lamination schemes, ply orientations, and multiple damaged elements in different damage scenarios. By evaluating the method on numerical results and comparing it with previous studies, its superiority is demonstrated. Furthermore, the proposed method exhibits robustness to changes in mass distribution in the system investigated by retrofitting extra masses to the plate structures that lead to worsening closely-spaced eigenvalues.

关键词： Structural health monitoring Frequency response function Laminated composite structures optimization algorithms Damping ratio Closely-spaced eigenvalues

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First-order algorithms for robust optimization problems via convex-concave saddle-point Lagrangian reformulation

arXiv

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arXiv 2021年

作者： Postek, Krzysztof Shtern, Shimrit Faculty of Electrical Enginnering Mathematics and Computer Science Delft University of Technology Delft Netherlands Faculty of Industrial Engineering and Management Technion - Israel Institute of Technology Haifa Israel

Robust optimization (RO) is one of the key paradigms for solving optimization problems affected by uncertainty. Two principal approaches for RO, the robust counterpart method and the adversarial approach, potentially lead to excessively large optimization problems. For that reason, first order approaches, based on online-convex-optimization, have been proposed Ben-Tal et al. (2015b), Ho-Nguyen and Kılınç-Karzan (2018) as alternatives for the case of large-scale problems. However, these methods are either stochastic in nature or involve a binary search for the optimal value. We show that this problem can also be solved with deterministic first-order algorithms based on a saddle-point Lagrangian reformulation that avoid both of these issues. Our approach recovers the other approaches’ O(1/ϵ2) convergence rate in the general case, and offers an improved O(1/ϵ) rate for problems with constraints which are affine both in the decision and in the uncertainty. Experiment involving robust quadratic optimization demonstrates the numerical benefits of our approach. Copyright © 2021, The Authors. All rights reserved.

关键词： optimization algorithms

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Estimating high dimensional monotone index models by iterative convex optimization

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Journal of Econometrics 2024年

作者： Khan, Shakeeb Lan, Xiaoying Tamer, Elie Yao, Qingsong Department of Economics Boston College United States Risk Division Goldman Sachs United States Department of Economics Harvard University United States Department of Economics Louisiana State University United States

In this paper we propose new approaches to estimating large dimensional monotone index models. This class of models has been popular in the applied and theoretical econometrics literatures as it includes discrete choice, nonparametric transformation, and duration models. A main advantage of our approach is computational. For instance, rank estimation procedures such as those proposed in Han (1987) and [7] that optimize a nonsmooth, nonconvex objective function are difficult to use with more than a few regressors, which limits their use with economic data sets. For such monotone index models with increasing dimension, we propose to use a new class of estimators based on batched gradient descent (BGD) involving nonparametric methods such as kernel estimation or sieve estimation, and study their asymptotic properties. The BGD algorithm uses an iterative procedure where the key step exploits a strictly convex objective function, resulting in computational advantages. A contribution of our approach is that our model is large dimensional and semiparametric so does not require the use of parametric distributional assumptions. © 2024 Elsevier B.V.

关键词： optimization algorithms

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Last-X-Generation Archiving Strategy for Multi-Objective Evolutionary algorithms 13

Last-X-Generation Archiving Strategy for Multi-Objective Evo...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Shu, Tianye Nan, Yang Shang, Ke Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Southern University of Science and Technology Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set selected from an external archive is usually superior to the final population. That is, the incorporation of an external archive improves the performance of EMOAs. However, the computation time for maintaining an external archive is long, especially when the archive size is large. To solve this issue, a simple archiving strategy is to save all solutions generated in the last several generations. In this paper, we examine this archiving strategy for three representative EMOAs on artificial test problems (Minus-DTLZ and WFG) and real-world problems (RE). Our results show that archiving the last several generations clearly improves the performance of EMOAs without severely increasing the computation time. © 2024 IEEE.

关键词： optimization algorithms

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An Efficient Data-Driven Framework for Detecting Infeasible Solutions in Multiobjective Evolutionary Bilevel optimization

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IEEE Transactions on Evolutionary Computation 2024年

作者： Mejia-De-Dios, Jesus-Adolfo Rodriguez-Molina, Alejandro Mezura-Montes, Efren Universidad Autonoma de Coahuila Unidad Saltillo Centro de Investigación en Matemáticas Aplicadas Coahuila Saltillo25280 Mexico Universidad Autónoma de la Ciudad de México Colegio de Ciencia y Tecnología Mexico City06720 Mexico University of Veracruz Artificial Intelligence Research Institute Veracruz Xalapa91097 Mexico

Detecting infeasible solutions is an important challenge in black-box Multiobjective Bilevel optimization (MOBO) due to a lower-level (LL) optimization problem used as a constraint (along with equality and inequality constraints) in an upper-level optimization. In this context, a feasible solution is an optimal solution to the LL problem, typically addressed using evolutionary algorithms (or other metaheuristics) for complex scenarios. Since metaheuristics do not guarantee optimality, then infeasible solutions are inherently reported. This paper introduces a novel data-driven framework to automatically identify infeasible solutions reported by Bilevel Evolutionary algorithms (BEA) when addressing any MOBO problem. This framework operates without imposing strong assumptions on objectives or constraints, making it versatile and easy to implement. Besides, our approach uses solutions reported by one or multiple BEAs to detect and eliminate possible infeasible solutions. This approach helps to enhance algorithm comparison by eliminating infeasible solutions before applying existing performance indicators. The framework is successfully applied to several MOBO problems, including two real-world instances from specialized literature, solved by four different BEAs. Results suggest that the proposed framework advances the field of bilevel evolutionary optimization, offering a tool for promoting fair algorithmic comparisons and ensuring solution feasibility without requiring a deep understanding of the problem context. © 1997-2012 IEEE.

关键词： optimization algorithms

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An Improved A∗ Algorithm for AUV Path Planning 2

An Improved A∗ Algorithm for AUV Path Planning

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2nd International Conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Zhu, Qi Xiang, Qing Cai, Limin School of Intelligent Manufacturing Jianghan University Wuhan China

ISBN: (纸本)9798350390254

In response to the problems of low search efficiency and a large number of redundant points traversed by the traditional A∗ algorithm in the path planning process of AUV, this article proposes an improved A∗ algorithm based path planning method for AUV. This method calculates the dynamic angle between the AUV and the target point at different positions, and dynamically measures the weight of the heuristic function;Then, a redundant point optimization strategy is adopted to optimize unnecessary redundant points and turning points in the path. The simulation experiment results show that, compared with the traditional A∗ algorithm, the heuristic improved A∗ algorithm based on redundant node optimization has reduced the number of searched nodes by 87.72%, 75.91%, and 85.75%, respectively, under three different starting and target points. The number of redundant nodes traversed by the algorithm has decreased by 9.09%, 33.33%, and 18.18%, indicating that the algorithm can effectively improve search efficiency and reduce the number of path turns. © 2024 IEEE.

关键词： optimization algorithms

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A Rational Cooperative Foraging Based Grey Wolf Optimizer 6th

A Rational Cooperative Foraging Based Grey Wolf Optimizer

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6th International Conference on Information Systems and Management Science, ISMS 2023

作者： Gaidhane, Prashant J. Adam, Shirish G. Mahajan, Nilesh S. Nerkar, Sachin S. Government College of Engineering Jalgaon India RC Patel Institute of Technology Shirpur Shirpur India

ISBN: (纸本)9783031707889

In this paper, a rational cooperative foraging based grey wolf optimizer (CFGWO) is proposed to enhance the performance of conventional grey wolf optimizer (GWO). It inherits the communicating behaviour of wolves during cooperative foraging in accordance with original leadership hierarchy based approach. The prime motivation is to direct every wolf through learning, communication, and cognitive processing. Here, wolves first learn through original leadership hierarchy and then transfer the information via communication signalling. Also, acceleration coefficient is introduced to balance communication between wolves throughout iterations. In sum, these modifications direct the searching towards potential region in effective manner, avoid stagnation at suboptimal solution, and evade premature convergence. CFGWO is investigated on 31 synthesis benchmark unconstrained test functions of distinct characteristics;and results are compared with other six prominent algorithms for various analysis metrics and few results are presented. CFGWO is a viable and robust alternative to solve global and engineering optimization problems with balanced exploration and exploitation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： optimization algorithms

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Multiobjective Many-Tasking Evolutionary optimization using Diversified Gaussian-Based Knowledge Transfer

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IEEE Transactions on Evolutionary Computation 2024年

作者： Lin, Qiuzhen Wang, Qianhui Chen, Baihao Ye, Yulong Ma, Lijia Tan, Kay Chen Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The Hong Kong Polytechnic University Department of Data Science and Artificial Intelligence Hong Kong Hong Kong

Multiobjective multitasking evolutionary algorithms have shown promising performance for tackling a set of multiobjective optimization tasks simultaneously, as the optimization experience gained within one task can be transferred to accelerate the solving of others. However, most studies only select similar transfer tasks based on their designed metrics, which become less efficient when tackling a large number of optimization tasks, as their transferred knowledge may be insufficiently diversified. To alleviate this issue, this article proposes a Multiobjective Many-Tasking Evolutionary Algorithm using Diversified Gaussian-based knowledge Transfer, named MMaTEA-DGT. In this algorithm, a diversified transfer selection strategy is presented to choose a number of similar and complementary source tasks for knowledge transfer. Then, based on the above diversified source tasks, a Gaussian-based transfer strategy is designed to transfer their various optimization knowledge. In this way, MMaTEA-DGT is more effective in transferring optimization knowledge to speed up the solving of many tasks. Experimental studies on both the benchmark suites and a real-world dynamic vaccine prioritization problem have indicated the superiority of MMaTEA-DGT over some recently proposed multiobjective many-tasking evolutionary algorithms. © 1997-2012 IEEE.

关键词： optimization algorithms

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