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arXiv 2023年

作者： Van Nguyen, David Doroudi, Shayan Epstein, Daniel A. School of Information and Computer Sciences University of California Irvine United States School of Education University of California Irvine United States

Our preliminary experiment examined a potential pain point with ASSIST, California’s database of articulation agreements. That pain point is cross-referencing multiple articulation agreements to manually develop an optimal academic plan. Optimal is defined as the minimal set of community college courses that satisfy all transfer requirements for the multiple universities a student is preparing to apply to. Accordingly, we designed a low-fidelity prototype that lists the minimal set of courses a hypothetical optimization algorithm would output based on selected articulation agreements. 24 students were tasked with creating an optimal academic plan using either ASSIST (which requires manual optimization) or the optimization prototype (which already provides the minimal set of classes). Prototype users had less optimality mistakes, were faster, and provided higher usability ratings compared to ASSIST users. Going forward, future research needs to move beyond our proof of value of a hypothetical optimization algorithm and towards actually implementing an algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary algorithms

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arXiv 2023年

作者： Bian, Chao Zhou, Yawen Li, Miqing Qian, Chao National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Artificial Intelligence Nanjing University Nanjing210023 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually performed in a greedy, deterministic manner. That is, the next-generation population is formed by selecting the best solutions from the current population and newly-generated solutions (irrespective of the selection criteria used such as Pareto dominance, crowdedness and indicators). In this paper, we analytically present that stochastic population update can be beneficial for the search of MOEAs. Specifically, we prove that the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, for solving two bi-objective problems, OneJumpZeroJump and bi-objective RealRoyalRoad, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed population update method. This work is an attempt to show the benefit of introducing randomness into the population update of MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

Multi-Layer Scheduling optimization for Intelligent Mobility of Maritime Operation 15

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Multi-Layer Scheduling Optimization for Intelligent Mobility...

15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhai, Deqing Fu, Xiuju Xu, Hai-Yan Yin, Xiao Feng Vasundhara, Jayaraman Zhang, Wanbing ASTAR Inst High Performance Comp Singapore 138632 Singapore

ISBN: (纸本)9781728151694

In this study, multi-layer scheduling optimization algorithms are proposed and validated based on historical vessel operation data in maritime terminals. The most relevant KPIs are average wait time, average turnaround time and berth occupancy rate presented in this study. Through the proposed optimization algorithms, the results shown that average wail time and turnaround time are significantly reduced with increasing of randomness threshold, which is a threshold to allow reschedules to buffer terminals. The average wait time and turnaround time are shortened by around 27.30 hrs (by 39.06%) and 39.41 hrs (by 27.18%), respectively. The berth occupancy rate of less utilized buffer terminals is also improved from 21.39% to 38.35%.

关键词： Big Data Automatic Identification System Scheduling optimization algorithms Maritime KPIs

Automatic mode-locking fiber lasers: progress and perspectives

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Science China(Information Sciences) 2020年第6期63卷 119-142页

作者： Guoqing PU Li ZHANG Weisheng HU Lilin YI State Key Lab of Advanced Communication Systems and Networks Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University

Polarization control in nonlinear polarization rotation based mode-locked fiber lasers is a longterm challenge. Suffering from the polarization drifts induced by environmental disturbances, nonlinear polarization rotation based mode-locked fiber lasers is difficult in continuously operating under the desired pulsation regime thereby substantially hindering their utilizations. The appearance of automatic modelocking techniques brings the light in addressing this challenge. Combining with various algorithms and electrical polarization control, automatic mode-locking techniques resolve the dilemma of nonlinear polarization rotation based mode-locked fiber lasers. We review the research progress of automatic mode-locking techniques in detail. Furthermore, we comment on the perspectives and potential applications of automatic mode-locking techniques.

关键词： nonlinear polarization rotation ultrashort pulses automatic mode-locking electrical polarization control optimization algorithms machine learning time stretch

Cubic Chaos Preference Multi-Objective optimization Algorithm with Adaptive Dual-Mode Mutation

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Journal of Computers (Taiwan) 2024年第6期35卷 29-43页

作者： Liu, Wenwu Kang, Lanlan Du, Qian Cao, Wenliang Zhao, Xiuniao School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China School of Information Engineering Gannan University of Science and Technology Ganzhou341000 China School of Information Science and Engineering Linyi University Linyi27600 China School of Electronic Information Dongguan Polytechnic Guangdong Dongguan523808 China

In the study of preference-based multi-objective optimization algorithms, the performance significantly depends on the preference information provided by the decision-maker. Over-reliance on this preference information can lead the algorithms to become trapped in locally optimal solutions, potentially overlooking high-quality solutions in other regions. Therefore, this paper proposes a Cubic Chaos Preference Multi-Objective optimization Algorithm with Adaptive Dual-mode Mutation (CPMOP-DM). Firstly, this algorithm utilizes the cubic chaos strategy to initialize population. This strategy possesses better chaos traversal and optimization speed and then, helps enhance the search breadth and global convergence of the optimization algorithm. Secondly, a dynamic focused preference exploration strategy is proposed to enhance the quality and satisfaction of the selected solutions. This strategy can gradually refine the search scope via, constructing dynamically shrinking exploration circles. Compared to traditional preference-based algorithms, experimental results demonstrate the competitiveness of the proposed algorithm. It effectively balances exploration and exploitation searching of the algorithm, thereby enhancing the algorithm’s diversity and distribution. It also avoids the local optimum problem caused by over-reliance on preference information. © 2024 Codon Publications. All rights reserved.

关键词： optimization algorithms

An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem

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arXiv 2023年

作者： Qiao, Gang Tewari, Ambuj Department of Statistics University of Michigan United States

We study the convex hull membership (CHM) problem in the pure exploration setting where one aims to efficiently and accurately determine if a given point lies in the convex hull of means of a finite set of distributions. We give a complete characterization of the sample complexity of the CHM problem in the one-dimensional case. We present the first asymptotically optimal algorithm called Thompson-CHM, whose modular design consists of a stopping rule and a sampling rule. In addition, we extend the algorithm to settings that generalize several important problems in the multi-armed bandit literature. Furthermore, we discuss the extension of Thompson-CHM to higher dimensions. Finally, we provide numerical experiments to demonstrate the empirical behavior of the algorithm matches our theoretical results for realistic time horizons. © 2023, CC BY.

关键词： optimization algorithms

A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization

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arXiv 2023年

作者： Dagréou, Mathieu Moreau, Thomas Vaiter, Samuel Ablin, Pierre Université Paris-Saclay Inria CEA Palaiseau France Université de Côte d’Azur CNRS LJAD Nice France Apple Paris France

Bilevel optimization problems, which are problems where two optimization problems are nested, have more and more applications in machine learning. In many practical cases, the upper and the lower objectives correspond to empirical risk minimization problems and therefore have a sum structure. In this context, we propose a bilevel extension of the celebrated SARAH algorithm. We demonstrate that the algorithm requires O((n + m)1/2 Ε−1) oracle calls to achieve Ε-stationarity with n + m the total number of samples, which improves over all previous bilevel algorithms. Moreover, we provide a lower bound on the number of oracle calls required to get an approximate stationary point of the objective function of the bilevel problem. This lower bound is attained by our algorithm, making it optimal in terms of sample complexity. © 2023, CC BY.

关键词： optimization algorithms

Optimal confidence interval for the difference of proportions

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arXiv 2023年

作者： Peer, Almog Azriel, David Technion - Israel Institute of Technology Israel

Estimating the probability of the binomial distribution is a basic problem, which appears in almost all introductory statistics courses and is performed frequently in various studies. In some cases, the parameter of interest is a difference between two probabilities, and the current work studies the construction of confidence intervals for this parameter when the sample size is small. Our goal is to find the shortest confidence intervals under the constraint of coverage probability being at least as large as a predetermined level. For the two-sample case, there is no known algorithm that achieves this goal, but different heuristics procedures have been suggested, and the present work aims at finding optimal confidence intervals. In the one-sample case, there is a known algorithm that finds optimal confidence intervals presented by Blyth and Still (1983). It is based on solving small and local optimization problems and then using an inversion step to find the global optimum solution. We show that this approach fails in the two-sample case and therefore, in order to find optimal confidence intervals, one needs to solve a global optimization problem, rather than small and local ones, which is computationally much harder. We present and discuss the suitable global optimization problem. Using the Gurobi package we find near-optimal solutions when the sample sizes are smaller than 15, and we compare these solutions to some existing methods, both approximate and exact. We find that the improvement in terms of lengths with respect to the best competitor varies between 1.5% and 5% for different parameters of the problem. Therefore, we recommend the use of the new confidence intervals when both sample sizes are smaller than 15. Tables of the confidence intervals are given in the Excel file in this link. © 2023, CC BY.

关键词： optimization algorithms

Prediction of small-scale piles by considering lateral deflection based on Elman Neural Network - Improved Arithmetic Optimizer algorithm

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ISA transactions 2022年 127卷 473-486页

作者： Ming Zhang Jianxun Yang Rongfu Ma Qian Du Dragan Rodriguez Shenzhen Bureau of Geology Shenzhen Guangdong 518023 China. Shenzhen Bureau of Geology Shenzhen Guangdong 518023 China. Electronic address: yangjx_gddz@***. Case Western Reserve University Cleveland OH USA.

Piles (kinds of geotechnical structures) are used for resisting various lateral loads including earthquakes and inclined loads. Hence, these structures' behavior under lateral load should be studied. Therefore, this investigation studies the lateral deflection (LD) of piles under different situations. 192 physical models were carried out by consideration of the most important factor on the lateral deflection amounts in dried sandy soils. Besides, a model of the Elman Neural Network (ENN) - Improved Arithmetic Optimizer (IAO) algorithm was suggested for predicting the piles' lateral deflection. For the intention of comparison, the Elman Neural Network model and Particle Swarm optimization - Artificial Neural Network were utilized in lateral deflection amounts estimation. For evaluating the proposed model validity, some parameters like Variance Account For, determination coefficient, and Root Mean Squared Error were estimated. The results showed the ENN-IAO method is more reliable for lateral deflection prediction in a small-scale pile in comparison to the ENN method and PSO-ANN model.

关键词： Elman Neural Network — Improved Arithmetic Optimizer algorithm Elman Neural Network, Lateral deflection optimization algorithms Particle Swarm optimization — Artificial Neural Network Small-scale pile

Estimating shear wave velocity in carbonate reservoirs from petrophysical logs using intelligent algorithms

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年 212卷

作者： Mehrad, Mohammad Ramezanzadeh, Ahmad Bajolvand, Mahdi Hajsaeedi, Mohammad Reza Shahrood Univ Technol Fac Min Petr & Geophys Engn Shahroud Semnan Province Iran

Shear-wave velocity (Vs) is a key petrophysical data for a wide spectrum of applications in the upstream oil industry. In many wells, however, the corresponding log cannot be acquired due to technical and/or cost-related issues. Appreciating the importance of this parameter, its relationship to other petrophysical logs has been extensively studied. For the most part, such studies focus on modes based on either rock physics, analytic equations, or artificial intelligence (AI). Inherent complexity of hydrocarbon reservoirs, especially carbonate ones, has made it difficult to build a comprehensive model of adequately high accuracy for various fields, keeping the research on novel models for such a purpose a still hot topic. This paper presents a high-accuracy high-generalizability model for predicting Vs from logging data. The required logs were acquired along three wells penetrating three carbonate reservoirs in SW Iran. In a preprocessing step, a robust regression technique was applied to identify and omit outliers. Subsequently, the data at two wells were used for training the model, with the data at the third well used for validating the trained model. Feature selection was performed by NSGA-II and five parameters were selected (Vp, Depth, RHOB, NPHI, and RT) as inputs to the model. For the first time, we employed the convolutional neural network (CNN) and multilayer extreme learning machine (MELM) in simple and hybrid forms with a few optimization algorithms, including particle swarm optimization (PSO), cuckoo optimization algorithm (COA), and genetic algorithm (GA) to build different models for predicting Vs from logging data. For the sake of comparison, we further applied the least-squares support-vector machine (LSSVM) in simple and hybrid forms with COA, PSO, and GA as well as a couple of popular analytic methods. Results of the training showed the superiority of the CNN, as measured by RMSE. Nevertheless, the MELM-COA model provided for much shorter learning

关键词： Extreme machine learning optimization algorithms Outlier detection Shear wave velocity estimation Deep learning