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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Meng, Xiang Pei, Yan Takagi, Hideyuki Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu965-8580 Japan University of Aizu Computer Science Division Aizuwakamatsu965-8580 Japan Kyushu University Fukuoka815-8540 Japan

ISBN: (纸本)9798350308365

This paper presents an innovative approach employing persistence-based clustering in Riemannian manifolds within evolutionary computation algorithms to address multi-modal optimization problems. The proposed framework is im-plemented and evaluated using the chaotic evolution algorithm. We introduce a novel algorithm named chaotic evolution with a clustering algorithm (CECA), which integrates the chaotic evolution characteristics from chaotic systems with the clustering method and Gaussian local search to solve multi-modal optimization problems. By leveraging chaotic dynamics, CECA enhances exploration and exploitation for efficient searching. Simultane-ously, it utilizes the clustering method to improve population diversity in the context of multi-modal optimization problems. The effectiveness and advantages of the proposed framework on the CECA algorithm are demonstrated through extensive experimental evaluations of various benchmark functions, in-cluding the congress on Evolutionary computation (CEC) con-ference functions. The experimental results indicate that the proposed framework exhibits distinct advantages in optimizing high-dimensional complex multi-modal functions. This study provides empirical evidence that persistence-based clustering in Riemannian manifolds constitutes an effective methodology for evolutionary multi-modal optimization. © 2024 IEEE.

关键词： Optimization algorithms

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RANDOMIZED RANGE-MAXIMA IN NEARLY-CONSTANT PARALLEL TIME 3rd

RANDOMIZED RANGE-MAXIMA IN NEARLY-CONSTANT PARALLEL TIME

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3RD international SYMP ON algorithms AND computation ( ISAAC 92 )

作者： BERKMAN, O MATIAS, Y VISHKIN, U Dept. of Computing School of Physical Sciences and Engineering King’s College London Strand London WC2R 2LS United Kingdom Institute for Advanced Computer Studies University of Maryland College Park 20742 MD United States Department of Computer Science Tel Aviv University Israel

ISBN: (纸本)3540562796

Given an array of n input numbers, the mnge. mazima problem is to preprocess the data so that queries of the type “what is the maximum value in subarray [i..j]” can be answered quickly using one processor. We present a randomized preprocessing algorithm that runs in O(log* n) time with high probability, using all optimal number of processors on a CRCW PRAM;each query can be processed in constant time by one processor. We also present a randomized algorithm for a parallel comparison model. Using an optimal number of processors, the preprocessing algorithm runs in O(α(n)) time with high probability;each query can be processed in O(α(n)) time by one processor. (α(n) is the inverse of Ackermann function.) A constunt time query can be achieved by some slowdown in the performance of the preprocessing stage. © Springer-Verlag Berlin Heidelberg 1992.

关键词： Artificial intelligence

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R-Peak Detection for ECG Signal Based on Local Maximums of Signal Magnitude and Correlation 12

R-Peak Detection for ECG Signal Based on Local Maximums of S...

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12th international congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Li, Jingbin Yu, Ya Jun Southern University of Science and Technology Deptartment of Electrical and Electronic Engineering Shenzhen China

ISBN: (纸本)9781728148526

Besides being used for heart disease diagnosis, ECG signal has been increasingly employed as monitoring of excise statues, emotions, stresses, and so on for healthy people. While most ECG detection algorithms were proposed by considering not only normal conditions, but also all kinds of abnormal conditions, these algorithms are usually over complex to be applied to healthy people. In this paper, we proposed a new method for the detection of R-peaks for healthy people based on the local maximums of magnitude and template correlation. A good trade-off between time complexity and detection precision is achieved by introducing a scheme to trigger the more complex template correlation detection when noise exists. In our test data of ECG signal from healthy people, the average sensitivity and precision achieved are 99.23% and 99.38%, respectively, while the computation complexity maintains low. © 2019 IEEE.

关键词： Electrocardiograms

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Confidence Bands Based on Rating Demonstrated on the CEC 2021 Competition Results 13

Confidence Bands Based on Rating Demonstrated on the CEC 202...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Ravber, Miha Mernik, Marjan Liu, Shih-Hsi Šmid, Marko Črepinšek, Matej University of Maribor Faculty of Electrical Engineering and Computer Science Koroška cesta 46 Maribor2000 Slovenia California State University Fresno Department of Computer Science FresnoCA93740 United States

ISBN: (纸本)9798350308365

Benchmarking plays a vital role in evaluating and comparing evolutionary algorithms. Our research addresses this critical need by introducing an innovative approach: rating-based confidence bands. This method serves to evaluate algorithm performance across various execution stages, enabling statistical comparison at different cutpoints of maximum Function Evaluations (maxFEs). Using publicly available results from the IEEE congress on Evolutionary computation (CEC) benchmark, we demonstrate the effectiveness of our methodology. Our approach uses the function error values recorded at distinct cutpoints to plot confidence bands. The confidence bands offer a comprehensive and intuitive comparison, revealing statistical significance in algorithm performances at different stages of execution. The method is integrated seamlessly into the EARS framework, facilitating the incorporation of additional algorithm results for a robust comparative analysis. Our results present a detailed analysis of CEC benchmark outcomes through rating-based confidence bands. This approach enables a nuanced evaluation of algorithmic performance, revealing statistical differences between algorithms at various cutpoints that were previously undisclosed. The framework encourages researchers to compare their results directly with state-of-the-art algorithms from benchmark competitions, enhancing result credibility and promoting a standardized evaluation approach within the research community. Our findings highlight the significance of our approach in advancing algorithm benchmarking practices. Furthermore, they emphasize the need for transparent evaluation of algorithms at different stages of execution. © 2024 IEEE.

关键词： benchmarking confidence bands evolutionary algorithms performance comparison rating intervals

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Can Big Population Always Bring Better Optimization Ability to Evolutionary computation for Large-Scale Optimization?

Can Big Population Always Bring Better Optimization Ability ...

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2022 IEEE international Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Jian, Jun-Rong Chen, Chun-Hua Liu, Dong Zhang, Jun Zhan, Zhi-Hui South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Guangzhou510006 China South China University of Technology School of Software Engineering Guangzhou510006 China Henan Normal University School of Computer and Information Engineering Xinxiang453007 China Hanyang University Ansan15588 Korea Republic of

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Artificial intelligence (AI) has fast developed nowadays especially in the deep learning field that most of the deep neural networks pursue the better problem-solving ability by making the network models deeper, larger, and more complex. However, too large and too complex AI algorithms/models require too large computational burden, which is not reality in academic researches nor the right way to real human intelligence. Moreover, in another research branch of AI named evolutionary computation (EC), which is inspired by the biological evolution of nature and swarm intelligence behaviours, will the EC algorithms become more efficient with larger and more complex algorithm model when solving complicated optimization problems like the large-scale optimization problems (LSOPs)? To this concern, this paper investigates whether some existing large-scale optimization EC algorithms can further improve their performance in solving LSOPs by only increasing the population size. We select 12 representative algorithms for investigation, including 4 standard EC algorithms and 8 well-known large-scale optimization algorithms. Then, we adopt the widely-used IEEE congress on Evolutionary computation (CEC 2010) LSOPs benchmark test suite to compare the performance of the same algorithms with different population sizes. The experimental results show that simply increasing the population size does not necessarily improve the performance of algorithms in solving LSOPs. © 2022 IEEE.

关键词： Evolutionary algorithms

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GPU accelerated CAD to inspection data deviation colormap generation

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Computer-Aided Design and Applications 2017年第2期14卷 234-241页

作者： Kurella, Venu Stone, Bob Spence, Allan McMaster University Canada Origin International Inc. Canada

Inspection of stamped sheet metal parts can require CAD to inspection data comparison rates exceeding 1 million points per second. With current serial CPU algorithms, comparing the nominal CAD to the digitizer data can take several minutes. The thousands of cores available with Graphical Processing Units (GPUs) therefore offer an attractive alternative. This paper describes GPU accelerated algorithms and memory management optimization for both point-facet and facet-facet CAD to inspection point cloud data deviation colormap generation. computation time reduction from over 4 minutes to 2 seconds – a speed-up of 124X – was achieved. In collaboration with an industry partner, the result is implemented as a practical Windows compatible DLL that can be deployed to the factory floor. © 2016 CAD Solutions, LLC.

关键词： Graphics processing unit

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PREDICTION OF NON-EQUILIBRIUM HEAT CONDUCTION USING PARALLEL computation OF THE PHONON BOLTZMANN TRANSPORT EQUATION

PREDICTION OF NON-EQUILIBRIUM HEAT CONDUCTION USING PARALLEL...

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ASME international Mechanical engineering congress and Exposition (IMECE)

作者： Ali, Syed A. Kollu, Gautham Mazumder, Sandip Sadayappan, P. Ohio State Univ Dept Mech & Aerosp Engn Columbus OH 43210 USA Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9780791849569

Non-equilibrium heat conduction, as occurring in modern-day sub-micron semiconductor devices, can be predicted effectively using the Boltzmann Transport Equation (BTE) for phonons. In this article, strategies and algorithms for large-scale parallel computation of the phonon BTE are presented. An unstructured finite volume method for spatial discretization is coupled with the control angle discrete ordinates method for angular discretization. The single-time relaxation approximation is used to treat phonon-phonon scattering. Both dispersion and polarization of the phonons are accounted for. Three different parallelization strategies are explored: (a) band-based, (b) direction-based, and (c) hybrid band/cell-based. Subsequent to validation studies in which silicon thin-film thermal conductivity was successfully predicted, transient simulations of non-equilibrium thermal transport were conducted in a three-dimensional device-like silicon structure, discretized using 604,054 tetrahedral cells. The angular space was discretized using 400 angles, and the spectral space was discretized into 40 spectral intervals (bands). This resulted in similar to 9.7X10(9) unknowns, which are approximately 3 orders of magnitude larger than previously reported computations in this area. Studies showed that direction-based and hybrid band/cell-based parallelization strategies resulted. in similar total computational time. However, the parallel efficiency of the hybrid band/cell-based strategy about 88% was found to be superior to that of the direction-based strategy, and is recommended as the preferred strategy for even larger scale computations.

关键词： Heat conduction Equilibrium (Physics) Phonons computation

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international Journal of Information Technology and Management 2016年第1期15卷 1-13页

作者： Wu, Jianzhang Yu, Xiao Gao, Wei School of Computer Science and Engineering Southeast University Nanjing210096 China School of Continuing Education Southeast University Nanjing210096 China School of Information Science and Technology Yunnan Normal University Kunming650500 China

In information retrieval, ontology is used to search the information which has highly semantic similarity of the original query concept, and return the results to the user. Ontology mapping is used to create the relationship between different ontologies, and the essence of which is similarity computation. In this article, we present new algorithms for ontology similarity measure and ontology mapping by determining the similarity matrix of ontology. The optimisation strategy and iterative procedure are designed in terms of metric distance learning tricks. The simulation experimental results show that the proposed new algorithms have high accuracy and efficiency on ontology similarity measure and ontology mapping in biology, physics applications, plant science and humanoid robotics. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Ontology

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A Niching-Based Reproduction and Preselection-Based Multiobjective Differential Evolution for Multimodal Multiobjective Optimization 13

A Niching-Based Reproduction and Preselection-Based Multiobj...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Lin, Hongyu Liang, Jing Yue, Caitong Wang, Yaonan School of Electrical Engineering Zhengzhou University Zhengzhou450001 China School of Electrical and Information Engineering Hunan University Changsha410000 China

ISBN: (纸本)9798350308365

In multimodal multiobjective optimization problems (MMOPs), there are several Pareto optimal solutions corresponding to the identical objective vector. MMOPs pose greater challenges for multiobjective optimization evolutionary algorithms (MOEAs) as they require balancing the convergence and diversity of the population in both the decision space and the objective space. Therefore, this paper proposes a novel coevolutionary multiobjective optimization differential evolution algorithm with a niching-based reproduction and a preselection-based environmental selection mechanism, called NPCMODE. The algorithm introduces a niching-based reproduction, evolving solutions independently within multiple niches to generate more dispersed solutions in the decision space. Additionally, a preselection-based environmental selection mechanism priori-tizes solutions with low density in both decision and objective spaces through a dual-population coevolutionary framework. The efficiency of NPCMODE is validated through comparative experiments with six representative multimodal multiobjective optimization evolutionary algorithms (MMOEAs) on the CEC 2019 benchmark suite, showcasing its effectiveness in achieving a balance between convergence and diversity performance. © 2024 IEEE.

关键词： Multiobjective optimization

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Exploring Generative AIs as Population Variation Operator in Multi-objective Optimization Problems 13

Exploring Generative AIs as Population Variation Operator in...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Ibarra-Vazquez, Gerardo Terashima-Marín, Hugo Coello, Carlos A. School of Engineering and Sciences Tecnologico de Monterrey Monterrey Mexico Departamento de Computacon CINVESTAV-IPN Mexico City Mexico

ISBN: (纸本)9798350308365

In recent years, evolutionary computation has signif-icantly advanced in processes related to machine learning. How-ever, the reciprocal integration of machine learning techniques into evolutionary computation remains relatively unexplored. Machine learning can substantially enhance the understanding of processes within Multi-Objective Evolutionary algorithms (MOEAs) by harnessing its proficiency in identifying patterns and employing data-driven approaches. Existing studies lack a comprehensive understanding of the intricate interaction between machine learning models and evolutionary algorithms, necessi-tating prioritized investigation to ensure the efficacy, reliabil-ity, and compatibility of integrated models within optimization frameworks. This paper addresses this gap by examining the behavior of using Generative Artificial Intelligence (AI) models as a population variation operator in Multi-objective Optimization Problems. Our experimental results reveal that Generative AI, particularly Distributional Adversarial Networks (DANs), sur-passes the performance of a traditional Generative Adversarial Network. Furthermore, DANs improve the population by gen-erating novel non-dominated solutions and augmenting overall performance and diversity. This study reveals the potential of the integration of Generative AI in evolutionary computation, presenting a pathway for advancements in addressing common challenges within multi-objective optimization problems. © 2024 IEEE.

关键词： Generative adversarial networks

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