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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Li, Yang Ji, Shihao Georgia State Univ Atlanta GA 30303 USA

ISBN: (纸本)9783030461478;9783030461461

We consider network sparsification as an L-0-norm regularized binary optimization problem, where each unit of a neural network (e.g., weight, neuron, or channel, etc.) is attached with a stochastic binary gate, whose parameters are jointly optimized with original network parameters. The Augment-Reinforce-Merge (ARM) [27], a recently proposed unbiased gradient estimator, is investigated for this binary optimization problem. Compared to the hard concrete gradient estimator from Louizos et al. [19], ARM demonstrates superior performance of pruning network architectures while retaining almost the same accuracies of baseline methods. Similar to the hard concrete estimator, ARM also enables conditional computation during model training but with improved effectiveness due to the exact binary stochasticity. Thanks to the flexibility of ARM, many smooth or non-smooth parametric functions, such as scaled sigmoid or hard sigmoid, can be used to parameterize this binary optimization problem and the unbiasness of the ARM estimator is retained, while the hard concrete estimator has to rely on the hard sigmoid function to achieve conditional computation and thus accelerated training. Extensive experiments on multiple public datasets demonstrate state-of-the-art pruning rates with almost the same accuracies of baseline methods. The resulting algorithm L-0-ARM sparsifies the Wide-ResNet models on CIFAR-10 and CIFAR-100 while the hard concrete estimator cannot. The code is public available at https://***/leo-yangli/l0-arm.

关键词： Network sparsification L-0-norm regularization binary optimization

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Differential Search Algorithm by XOR Gate in binary optimization Problems 57

Differential Search Algorithm by XOR Gate in Binary Optimiza...

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57th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST)

作者： Sahman, Mehmet Akif Cinar, Ahmet Cevahir Selcuk Univ Fac Technol Selcuk Univ Campus TR-42075 Konya Turkey

ISBN: (纸本)9781665485005

There are many meta-heuristic algorithms proposed by researchers, and the general feature of these algorithms is that they are modeled by being inspired by the intelligent behaviors of living things in nature, individually or as a group. Basic versions of meta-heuristic algorithms are frequently introduced to solve continuous optimization problems. However, due to their flexibility, they can be adjusted for use in various types of optimization problems. In this research, an improved version of the Differential Search Algorithm (DSAX), which is a population-based meta-heuristic algorithm that has shown its success in solving optimization problems, is proposed to use in solving binary optimization problems. The DSAX has been tested on large-scale binary optimization problems. Moreover, it is compared with the XOR-Based version of the Tree-Seed Algorithm, which is well known in the literature. According to the obtained results and the comparisons, it is seen that the DSAX algorithm proposed in this study is superior.

关键词： Meta-Heuristics Differential Search Algorithm XOR logic gate binary optimization

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Modeling and performance evaluation of TSO-preserving binary optimization

Modeling and performance evaluation of TSO-preserving binary...

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20th International Conference on Parallel Architectures and Compilation Techniques, PACT 2011

作者： Wang, Cheng Wu, Youfeng Programming Systems Lab. Microprocessor and Programming Research Intel Labs. 2200 Mission College Blvd. Santa Clara CA 95052 United States

ISBN: (纸本)9780769545660

Program optimization on multi-core systems must preserve the program memory consistency. This paper studies TSO-preserving binary optimization. We introduce a novel approach to formally model TSO-preserving binary optimization based on the formal TSO memory model. The major contribution of the modeling is a sound and complete algorithm to verify TSO-preserving binary optimization with O(N2) complexity. We also developed a dynamic binary optimization system to evaluate the performance impact of TSO-preserving optimization. We show in our experiments that, dynamic binary optimization without memory optimizations can improve performance by 8.1%. TSOpreserving optimizations can further improve the performance by 4.8% to a total 12.9%. Without considering the restriction for TSO-preserving optimizations, the dynamic binary optimization can improve the overall performance to 20.4%. © 2011 IEEE.

关键词： binary optimization Heuristic algorithms Instruction sets Load modeling Memory management optimization TSO-preserving binary optimization TSO-preserving optimization Total Store Ordering dynamic binary optimization formal TSO memory model multicore system

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STOCHASTIC LEARNING APPROACH FOR binary optimization: APPLICATION TO BAYESIAN OPTIMAL DESIGN OF EXPERIMENTS*

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2022年第2期44卷 B395-B427页

作者： ATTIA, A. H. M. E. D. LEYFFER, S. V. E. N. MUNSON, T. O. D. D. S. Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA

We present a novel stochastic approach to binary optimization suited for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations. The OED utility function, namely, the regularized optimality criterion, is cast into a stochastic objective function in the form of an expectation over a multivariate Bernoulli distribution. The probabilistic objective is then solved by using a stochastic optimization routine to find an optimal observational policy. This formulation (a) is generally applicable to binary optimization problems with soft constraints and is ideal for OED and sensor placement problems;(b) does not require differentiability of the original objective function (e.g., a utility function in OED applications) with respect to the design variable, and thus it enables direct employment of sparsityenforcing penalty functions such as \ell 0, without needing to utilize a continuation procedure or apply a rounding technique;(c) exhibits much lower computational cost than traditional gradient-based relaxation approaches;and (d) can be applied to both linear and nonlinear OED problems with proper choice of the utility function. The proposed approach is analyzed from an optimization perspective with detailed convergence analysis of the optimization approach and is also analyzed from a machine learning perspective with correspondence to policy gradient reinforcement learning. The approach is demonstrated numerically by using an idealized two-dimensional Bayesian linear inverse problem and validated by extensive numerical experiments carried out for sensor placement in a parameter identification setup.

关键词： design of experiments binary optimization Bayesian inverse problem reinforcement learning stochastic optimization

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A reinforcement learning based computational intelligence approach for binary optimization problems: The case of the set-union knapsack problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 118卷

作者： Ozsoydan, Fehmi Burcin Golcuk, Ilker Dokuz Eylul Univ Fac Engn Dept Ind Engn TR-35397 Izmir Turkey Izmir Bakircay Univ Dept Ind Engn TR-35665 Izmir Turkey

As today's one of the hottest topics, machine learning brings about opportunities in various research areas. Moreover, computational intelligence and metaheuristics open up new strategies, which are shown to be efficient in solving optimization problems. However, studies bringing such remarkable approaches together are still lacking. In this context, the present paper introduces a Q-learning reinforcement learning strategy for binary optimization problems. The developed algorithm works as a reinforcement and recommendation system that evaluates the used algorithms, assigns rewards, promotes or demotes them. Thus, it invokes more promising optimizers more frequently. The proposed Q-learning algorithm uses Particle Swarm optimization (PSO), Genetic Algorithm and a hybrid of these algorithms, namely, genetic-based PSO (gbPSO) as optimizers. Therefore, it is aimed to avoid local optima by using various optimizers and gathering additional statistical data. Secondarily, all optimizers are further enhanced by adopting an initial solution generation technique and triggered random immigrants mechanism to preserve swarm diversity. In addition to these procedures, a mutation procedure that decreases the diversity is adopted. Thus, more intensified search is encouraged towards the end of search. Moreover, while PSO requires for transfer functions in order to perform in binary spaces, the adopted and further improved gbPSO does not necessarily need such auxiliary procedures. Finally, the performances of all used algorithms are analysed on a recently caught on binary problem, namely, the set-union knapsack problem, which has a wide range of real-life applications. As demonstrated by the comprehensive experimental study and appropriate statistical tests, promising improvements are achieved.

关键词： Machine learning Q-learning Genetic algorithm Particle swarm optimization binary optimization Knapsack problem

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JayaX: Jaya algorithm with xor operator for binary optimization

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APPLIED SOFT COMPUTING 2019年 82卷 105576-000页

作者： Aslan, Murat Gunduz, Mesut Kiran, Mustafa Servet Sirnak Univ Fac Engn Dept Comp Engn Sirnak Turkey Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn TR-42075 Konya Turkey

Jaya is a population-based heuristic optimization algorithm proposed for solving constrained and unconstrained optimization problems. The peculiar distinct feature of Jaya from the other population-based algorithms is that it updates the positions of artificial agent in the population by considering the best and worst individuals. This is an important property for the algorithm to balance exploration and exploitation on the solution space. However, the basic Jaya cannot be applied to binary optimization problems because the solution space is discretely structured for this type of optimization problems and the decision variables of the binary optimization problems can be element of set [0,1]. In this study, we first focus on discretization of Jaya by using a logic operator, exclusive or - xor. The proposed idea is simple but effective because the solution update rule of Jaya is replaced with the xor operator, and when the obtained results are compared with the state-of-art algorithms, it is seen that the Jaya-based binary optimization algorithm, JayaX for short, produces better quality results for the binary optimization problems dealt with the study. The benchmark problems in this study are uncapacitated facility location problems and CEC2015 numeric functions, and the performance of the algorithms is compared on these problems. In order to improve the performance of the proposed algorithm, a local search module is also integrated with the JayaX. The obtained results show that the proposed algorithm is better than the compared algorithms in terms of solution quality and robustness. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Jaya binary optimization Logic operator Exclusive or

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Revisiting some classical linearizations of the quadratic binary optimization problem and linkages with constraint aggregations

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DISCRETE optimization 2024年 54卷

作者： Punnen, Abraham P. Dhanda, Navpreet Kaur Simon Fraser Univ Dept Math Surrey BC Canada

In this paper, we examine explicit linearization models associated with the quadratic unconstrained binary optimization problem (QUBO). After reviewing some well-known basic linearization techniques, we present a new explicit linearization technique (PK) for QUBO with interesting properties. In particular, PK yields the same LP relaxation value as that of the standard linearization of QUBO while employing less number of constraints. We then show that using weighted aggregations of selected constraints of the basic linearization models, several new and effective linearization models of QUBO can be developed. Although aggregation of constraints has been studied in the past for solving systems of Diophantine equations in non- negative variables, none of them resulted in practical models, particularly due to the large size of the associated multipliers. For our aggregation based models, the multipliers can be of any positive real number. Moreover, we show that by choosing the multipliers appropriately, the LP relaxations of the resulting linearizations have optimal objective function values identical to that of the corresponding non-aggregated models. Theoretical and experimental comparisons of the new and existing explicit linearization models are also provided. Although our discussions were focused primarily on QUBO, the models and results we obtained extend naturally to the quadratic binary optimization problem with linear constraints.

关键词： QUBO binary optimization Integer programming Linearizations Experimental analysis

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binary-optimization-based Multilayers and Their Practical Applications

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CURRENT OPTICS AND PHOTONICS 2024年第6期8卷 545-561页

作者： Park, Geon-Tae Kang, Rira Lee, Byunghong Kim, Sun-Kyung Kyung Hee Univ Dept Appl Phys Yongin 17104 South Korea Hyundai Motor Co Adv Vehicle Platform Div Energy Device Res Team Uiwang 16082 South Korea

Multilayers composed of two or more materials enable the regulation of transmission, reflection, and absorption spectra across one or multiple bands. While analytic formulas based on well-established interference conditions, such as those employed in single-, double-, and triple-layer antireflective coatings and distributed Bragg reflectors, have provided suitable solutions for traditional optical coatings, they are limited in achieving the intricate spectral characteristics required by multifunctional optical coatings. To overcome this limitation, a variety of machine learning-based design algorithms have been rigorously studied. Among these, binary optimization has proven particularly effective for designing multi- layer optical coatings. This approach transforms a given multilayer into a binary vector with multiple bits, where each bit represents one of the constituent materials, and quickly identifies an optimal figure- of-merit by analyzing the interactions among the elements of the binary vector. In this review article, we elucidate the principles of binary optimization and explore its applications in the design of multilayers for antireflective coatings for high-numerical-aperture lenses, transparent radiative coolers for energy- saving windows, and bandpass filters for thermophotovoltaics. Furthermore, we address the limitations, challenges, and perspectives of machine learning-based optical design to guide directions for future research in this field.

关键词： binary optimization Machine learning Multilayer Optical coating Optical design

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binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets

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ENGINEERING optimization 2019年第3期51卷 369-389页

作者： Reddy, Srikanth K. Panwar, Lokesh Panigrahi, B. K. Kumar, Rajesh IIT Dept Elect Engn Delhi India IIT Ctr Energy Studies Delhi India MNIT Dept Elect Engn Jaipur Rajasthan India

This article presents a metaheuristic approach, the binary whale optimization algorithm (BWOA), to solve complex, constrained, non-convex, binary-nature profit-based unit commitment (PBUC) optimization problems of a price-taking generation company (GenCo) in the electricity market. To simulate the binary-nature PBUC problem, the continuous, real-value whale position/location is mapped into binary search space through various transfer functions. This article introduces three variants of BWOA using tangential hyperbolic, inverse tangent (arctan) and sigmoidal transfer functions. The effectiveness of the BWOA approaches is examined in test systems with different market mechanisms, i.e. an energy-only market, and energy and reserve market participation with different reserve payment methods. The simulation results are presented, discussed and compared with other existing approaches. The convergence characteristics, solution quality and consistency of the results across different BWOA variants are discussed. The superiority and statistical significance of the proposed approaches with respect to existing approaches is also presented.

关键词： binary optimization profit-based unit commitment meta-heuristic optimization electricity market binary whale optimization

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BAOA: binary Arithmetic optimization Algorithm With K-Nearest Neighbor Classifier for Feature Selection

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IEEE ACCESS 2023年 11卷 94094-94115页

作者： Khodadadi, Nima Khodadadi, Ehsan Al-Tashi, Qasem El-Kenawy, El-Sayed M. Abualigah, Laith Abdulkadir, Said Jadid Alqushaibi, Alawi Mirjalili, Seyedali Univ Miami Dept Civil & Architectural Engn Coral Gables FL 33146 USA Univ Arkansas Dept Chem & Biochem Fayetteville AR 72701 USA Univ Texas MD Anderson Canc Ctr Dept Imaging Phys Houston TX 77094 USA Albaydha Univ Fac Adm & Comp Sci Albaydha Yemen Delta Higher Inst Engn & Technol Dept Commun & Elect Mansoura 35111 Egypt Al al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 11022801 Lebanon Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar 32610 Malaysia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane SA 4006 Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

The Arithmetic optimization Algorithm (AOA) is a recently proposed metaheuristic algorithm that has been shown to perform well in several benchmark tests. The AOA is a metaheuristic that uses the main arithmetic operators' distribution behavior, such as multiplication, division, subtraction, and addition. This paper proposes a binary version of the Arithmetic optimization Algorithm (BAOA) to tackle the feature selection problem in classification. The algorithm's search space is converted from a continuous to a binary one using the sigmoid transfer function to meet the nature of the feature selection task. The classifier uses a method known as the wrapper-based approach K-Nearest Neighbors (KNN), to find the best possible solutions. This study uses 18 benchmark datasets from the University of California, Irvine (UCI) repository to evaluate the suggested binary algorithm's performance. The results demonstrate that BAOA outperformed the binary Dragonfly Algorithm (BDF), binary Particle Swarm optimization (BPSO), binary Genetic Algorithm (BGA), and binary Cat Swarm optimization (BCAT) when various performance metrics were used, including classification accuracy, selected features as well as the best and worst optimum fitness values.

关键词： Feature extraction Machine learning algorithms Classification algorithms Arithmetic Heuristic algorithms Task analysis Computational modeling binary codes optimization methods Feature selection binary optimization arithmetic optimization algorithm classification

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