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Using the Empirical Attainment Function for Analyzing Single-objective Black-box optimization algorithms

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arXiv 2024年

作者： López-Ibáñez, Manuel Vermetten, Diederick Dreo, Johann Doerr, Carola Alliance Manchester Business School University of Manchester ManchesterM13 9PL United Kingdom Leiden Institute for Advanced Computer Science Leiden Netherlands Pasteur Institute Université Paris Cité Bioinformatics and Biostatistics Hub Department of Computational Biology Systems Biology Group Paris France Sorbonne Université CNRS LIP6 Paris France

A widely accepted way to assess the performance of iterative black-box optimizers is to analyze their empirical cumulative distribution function (ECDF) of pre-defined quality targets achieved not later than a given runtime. In this work, we consider an alternative approach, based on the empirical attainment function (EAF) and we show that the target-based ECDF is an approximation of the EAF. We argue that the EAF has several advantages over the target-based ECDF. In particular, it does not require defining a priori quality targets per function, captures performance differences more precisely, and enables the use of additional summary statistics that enrich the analysis. We also show that the average area over the convergence curves is a simpler-to-calculate, but equivalent, measure of anytime performance. To facilitate the accessibility of the EAF, we integrate a module to compute it into the IOHanalyzer platform. Finally, we illustrate the use of the EAF via synthetic examples and via the data available for the BBOB suite. © 2024, CC BY.

关键词： optimization algorithms

Discrete Parameter Simulation optimization algorithms with Applications to Admission Control with Dependent Service Times

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Discrete Parameter Simulation Optimization Algorithms with A...

Proceedings of the 46th IEEE Conference on Decision and Control, Pages 919-1382

作者： Vivek Mishra Shalabh Bhatnagar N. Hemachandra Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Industrial Engineering and Operations Research Indian Institute of Technology Mumbai India

ISBN: (纸本)9781424414970;1424414970

We propose certain discrete parameter variants of well known simulation optimization algorithms. Two of these algorithms are based on the smoothed functional (SF) technique while two others are based on the simultaneous perturbation stochastic approximation (SPSA) method. They differ from each other in the way perturbations are obtained and also the manner in which projections and parameter updates are performed. All our algorithms use two simulations and two-timescale stochastic approximation. As an application setting, we consider the important problem of admission control of packets in communication networks under dependent service times. We consider a discrete time slotted queueing model of the system and consider two different scenarios - one where the service times have a dependence on the system state and the other where they depend on the number of arrivals in a time slot. Under our settings, the simulated objective function appears ill-behaved with multiple local minima and a unique global minimum characterized by a sharp dip in the objective function in a small region of the parameter space. We compare the performance of our algorithms on these settings and observe that the two SF algorithms show the best results overall. In fact, in many cases studied, SF algorithms converge to the global minimum.

关键词： Admission control parameter space methods algorithms Global Minimum optimization algorithms Service time simulating Objective function stochastic approximation networks(communications) arrivals discrete-time

Automatic Image Enhancement Using Evolutionary optimization algorithms

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Automatic Image Enhancement Using Evolutionary Optimization ...

IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

作者： Varun Saxena Deeksha Goel Tarun Kumar Rawat Electronics and Communication Engineering Division Netaji Subhas Institute of Technology

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

This paper presents a novel method of improving image quality using meta-heuristic algorithms such as Bat Algorithm, Cuckoo Search Algorithm and Interior Search Algorithm by contrast enhancement method. The performance of these algorithms are compared based on their ability to enhance the contrast, detail and sharpness of the gray scale image. These algorithms are used to generate optimal parameters to reach the global best of a fitness function which is then used to judge the enhancement achieved for the given image.

关键词： Image enhancement Cuckoo search algorithm Bat algorithm Interior search algorithm Hybrid intelligent optimization image enhancement SEARCHING ALGORITHM optimization algorithms contrast enhancement method Meta-heuristic algorithms Image quality algorithms optimal parameter Sharpness

Absolute Ranking: An Essential Normalization for Benchmarking optimization algorithms

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arXiv 2024年

作者： Jing, Yunpeng Liu, Qunfeng The School of Computer Science and Technology Dongguan University of Technology Dongguan523808 China

Evaluating performance across optimization algorithms on many problems presents a complex challenge due to the diversity of numerical scales involved. Traditional data processing methods, such as hypothesis testing and Bayesian inference, often employ ranking-based methods to normalize performance values across these varying scales. However, a significant issue emerges with this ranking-based approach: the introduction of new algorithms can potentially disrupt the original rankings. This paper extensively explores the problem, making a compelling case to underscore the issue and conducting a thorough analysis of its root causes. These efforts pave the way for a comprehensive examination of potential solutions. Building on this research, this paper introduces a new mathematical model called "absolute ranking" and a sampling-based computational method. These contributions come with practical implementation recommendations, aimed at providing a more robust framework for addressing the challenge of numerical scale variation in the assessment of performance across multiple algorithms and problems. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

Performance Comparison of Phoneme Modeling using MFCC and IHCC Features with various optimization algorithms for Neural Network Architectures

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Performance Comparison of Phoneme Modeling using MFCC and IH...

International Conference on Advanced Computer Control, ICACC

作者： Beneeta Rozario Abraham Thomas Dominic Mathew Dept. of Applied Electronics and Instrumentation Rajagiri School of Engineering and Technology Kochi India

ISBN: (纸本)9781728155241

Human speech signal is rich with information such as the identity of the speaker, the spoken message, emotional and physical state of the speaker, the spoken language, gender, age and other information. Automatic Speech Recognition (ASR) involves complex tasks aimed at recognition and translation of human speech to text by computers. Phoneme Recognition means recognizing the phonemes associated with speech utterance and is a part of ASR. Developing a phonetic engine and enhancing its performance can lead to significant improvement in ASR. In this paper we propose Artifical Neural Network (ANN) based Phoneme Modeling. We compare performance of speech features such as Inner Hair Cell Coefficients (IHCC) and Mel-Frequency Cepstral Coefficient (MFCC) using various neural network architectures, different optimization algorithms, change in input data vector dimensionality (corresponding to different contextual information) and also increasing the number of epochs. Experiments were carried out on TIMIT database. Our experimental results indicates MFCC performs much better than IHCC feature and the best optimization algorithm is found to be SGD for IHCC and Adagrad for MFCC features by trying out with different sizes of input vector, changing the number of training iterations, training algorithms for various neural network architectures.

关键词： cepstral analysis learning (artificial intelligence) neural nets optimisation speech recognition Speech recognition optimization algorithms Mel frequency cepstral coefficient Neural network Cepstral analysis aquifer storage and recovery ARCHITECTURE Speech

Quantum Approximate optimization algorithms for Maximum Cut on Low-Girth Graphs

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arXiv 2024年

作者： Li, Tongyang Su, Yuexin Yang, Ziyi Zhang, Shengyu School of Computer Science Peking University China Center on Frontiers of Computing Studies Peking University China School of Mathematical Science Peking University China Tencent Quantum Laboratory China

Maximum cut (MaxCut) on graphs is a classic NP-hard problem. In quantum computing, Farhi, Gutmann, and Goldstone proposed the Quantum Approximate optimization Algorithm (QAOA) for solving the MaxCut problem. Its guarantee on cut fraction (the fraction of edges in the output cut over all edges) was mainly studied for high-girth graphs, i.e., graphs with only long cycles. On the other hand, low-girth graphs are ubiquitous in theoretical computer science, including expander graphs being outstanding examples with wide applications in theory and beyond. In this paper, we apply QAOA to MaxCut on a set of expander graphs proposed by Mohanty and O’Donnell known as additive product graphs. Additionally, we apply multi-angle QAOA (ma-QAOA) to better utilize the graph structure of additive product graphs in ansatz design. In theory, we derive an iterative formula to calculate the expected cut fraction of such graphs. This formula also extends to the quantum MaxCut problem. On the other hand, we conduct numerical experiments to compare between best-known classical local algorithms and QAOA with constant depth. Our results demonstrate that QAOA outperforms the best-known classical algorithms by 0.3% to 5.2% on several additive product graphs, while ma-QAOA further enhances this advantage by an additional 0.6% to 2.5%. In particular, we observe cases that ma-QAOA exhibits superiority over best-known classical algorithms but QAOA does not. Furthermore, we extend our experiments to planar graphs such as tiling grid graphs, where QAOA also demonstrates an advantage. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

Enhancing machine learning optimization algorithms by leveraging memory caching

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Enhancing machine learning optimization algorithms by levera...

International Conference on High Performance Computing and Simulation

作者： Imen Chakroun Tom Vander Aa Tom Ashby Exascience Life Lab. IMEC Leuven Belgium

ISBN: (纸本)9781538678800

Searching a solution space using Stochastic Gradient Descent (SGD) depends on the examples picked at each iteration of the algorithm. Therefore, best practices suggest randomizing the order of training points to visit after every epoch. This random selection is typically implemented as a random shuffling of the order of the training vectors rather than a genuine random training point selection. The shuffling is usually performed after every epoch which results in an extremely low temporal locality of access to the training set. Indeed, each training point is used once, and not before all the other training points have been visited. This means that a cache layer in the memory hierarchy of a modern HPC computer system will have little benefit for the algorithm unless all the training points fit inside that cache.

关键词： Training Machine learning optimization Convergence Computational modeling Machine learning algorithms Stochastic processes Training Machine learning algorithms Functional training Markov chain Computational modeling optimization algorithms Cache memory Converge Artificial Intelligence Machine Learning memory hierarchy Solution space Stochastic Processes Iterative algorithms

EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic optimization algorithms

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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic optimization algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： optimization algorithms

Synthesis of EBG Surfaces Using Evolutionary optimization algorithms

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Synthesis of EBG Surfaces Using Evolutionary Optimization Al...

European Conference on Antennas and Propagation

作者： L. Deias G. Mazzarella N. Sirena DIEE - Department of Electrical and Electronic Engineering University of Cagliari DIEE and Consorzio COSMOLAB

In this paper evolutionary computation is applied to the synthesis of planar periodic EBG. We constrained our evolutionary design to the unit cell geometry and used a full-wave MoM to evaluate all individuals. Results... 详细信息

ISBN: (纸本)9781424447534

关键词： optimization algorithms synthesis unit cell design by evolution resonant frequencies Bandwidth Evolutionary evolutionary algorithm full-wave METHOD OF MOMENTS Surfaces