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An Efficient optimization of the Monte Carlo Tree Search algorithm for Amazons

algorithmS 2024年第8期17卷 334页

作者： Zhang, Lijun Zou, Han Zhu, Yungang Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China

Amazons is a computerized board game with complex positions that are highly challenging for humans. In this paper, we propose an efficient optimization of the Monte Carlo tree search (MCTS) algorithm for Amazons, fusing the 'Move Groups' strategy and the 'Parallel Evaluation' optimization strategy (MG-PEO). Specifically, we explain the high efficiency of the Move Groups strategy by defining a new criterion: the winning convergence distance. We also highlight the strategy's potential issue of falling into a local optimum and propose that the Parallel Evaluation mechanism can compensate for this shortcoming. Moreover, We conducted rigorous performance analysis and experiments. Performance analysis results indicate that the MCTS algorithm with the Move Groups strategy can improve the playing ability of the Amazons game by 20-30 times compared to the traditional MCTS algorithm. The Parallel Evaluation optimization further enhances the playing ability of the Amazons game by 2-3 times. Experimental results show that the MCTS algorithm with the MG-PEO strategy achieves a 23% higher game-winning rate on average compared to the traditional MCTS algorithm. Additionally, the MG-PEO Amazons program proposed in this paper won first prize in the Amazons Competition at the 2023 China Collegiate Computer Games Championship & National Computer Games Tournament.

关键词： Amazons algorithm optimization Move Groups Parallel Evaluation

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Theory and optimization of double-walled carbon nanotube reinforced rubber composites for underwater sound absorption

Results in Engineering

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Results in Engineering 2025年

作者： Junyu Li Xiaomeng Li Siyang Li Shande Li Zhifu Zhang School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Hubei Institute of Specialty Vehicle Suizhou 441300 China School of Mechanical and Electrical Engineering Hainan University Haikou 570228 China

This study explores the application of double-walled carbon nanotubes (DWCNTs) in underwater acoustic materials, aiming to overcome the low-frequency absorption limitations of traditional materials. Four main aspects were investigated: first, the mechanical properties of armchair-type DWCNTs were calculated based on molecular dynamics, and the equivalent mechanical parameters of the DWCNTs-reinforced rubber composites were computed by the Halpin-Tsai model. Secondly, a 6×6 transfer matrix model based on orthotropic anisotropic materials was established to predict sound absorption properties, validated by COMSOL Multiphysics simulations. Again, the influence laws of six micro-macro key parameters of the reinforcing materials on the sound absorption characteristics were explored. Finally, a comprehensive multi-gradient and multi-parameter optimization study of the underwater acoustic functional material was carried out by the Bayesian optimization and Hyperband (BOHB) optimization algorithm. The absorption bandwidth (α ≥ 0.65) of the optimized underwater acoustic functional composites spans from 0.299 kHz to 20 kHz, indicating its broadband absorption capability for practical applications. These findings advance the development of enhanced underwater acoustic materials.

关键词： molecular dynamics transfer matrix method sound absorption algorithm optimization

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Geochronouting Based optimization of the Information Processing algorithm for Positioning of Receiving Antenna of Stationary Hydroacoustic Complexes

Geochronouting Based Optimization of the Information Process...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： A. A. Ovodenko E. G. Semenova V. M. Balashov Ya. A. Ivakin E. A. Frolova Saint-Petersburg State University of Aerospace Instrumentation St. Petersburg Russia «NPP "Radar mms"» JSC St. Petersburg Russia Saint-Petersburg State University of Aerospace Instrumentation «Concern "Oceanpribor" »JSC Saint-Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences St. Petersburg Russia

ISBN: (数字)9781728149448

ISBN: (纸本)9781728149455

The article considers the task of optimization of the information processing algorithm for positioning of receiving antenna of stationary hydroacoustic complexes based on geochronotracking and statistical evaluation of retrospective data on the functioning efficiency. The features of the algorithms for processing the positioning information of receiving antennas based on geochronotracking largely determine the effectiveness and accuracy of the process of purpose-aimed using of sonar systems. This fact has determined the need to set the corresponding optimization problem, establish the boundary conditions for its solution and search for the corresponding extreme points. This article is devoted to the consideration of the mathematical and systemological aspects of the presented optimization. It defines the main parameters and optimality conditions of the considered algorithm, takes into account the results of recent developments on the subject of geochronological tracking.

关键词： sonar complexes and systems geochronological track and tracking rational algorithm algorithm optimization oceanographic research optimization problem statement geographic information system spatial data algorithm optimization Receiving antennas spatial data geographic information systems Patient Positioning Oceanographic research underwater sound algorithms Sonar equipment Information Processing Automatic data processing

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Research Progress of Intelligent optimization algorithm in Big Data Background

Research Progress of Intelligent Optimization Algorithm in B...

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2nd International Conference on Artificial Intelligence and Engineering Applications(AIEA 2017)

作者： DUO ZHANG College of Management and Economics Tianjin University

Big data is the inevitable outcome of the rapid development of modern information technology, effective analysis and processing of big data will not only bring great economic value, but will also promote social development. Under the big data environment, the data scale, speed of emergence and its difficulty make optimizing issues very complex. In recent decades, genetic algorithm(GA), particle swarm optimization(PSO), ant colony optimization(ACO), Artificial Fish School algorithm, Bacteria Foraging optimization algorithm(BFOA), artificial neural networks(ANNs) and other multi-population intelligent algorithms appeared. In this paper, several typical intelligent optimization algorithms are introduced, including genetic algorithm, particle swarm optimization algorithm, ant colony algorithm, artificial fish swarm algorithm and bacterial foraging algorithm. The basic principles of five algorithms are described respectively, along with the direction of improvement and feasible applications.

关键词： Big data intelligent optimization algorithm algorithm optimization genetic algorithm

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Application and optimization of the UCT algorithm in Einstein wurfelt nicht!

Application and optimization of the UCT algorithm in Einstei...

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第34届中国控制与决策会议

作者： Tianlong Wang Zhen Yang Zhengbo Xie School of Computer Science Beijing Institute of Technology

Based on the characteristics of Einstein würfelt nicht!(EWN),this paper puts forward the UCT algorithm applied to EWN,and on its basis,an optimized UCT algorithm with the evaluation function and the dynamic searching rounds is *** evaluation function is used to better evaluate the situation,and the dynamic searching rounds is used to reduce the average cost per game by a decay *** playing multiple games with the UCT algorithm,the optimized UCT algorithm proved to be effective in improving the strength of the EWN game system.

关键词： Einstein würfelt nicht! Monte Carlo Tree Search UCT algorithm optimization

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Basics of genetic algorithms optimization for RAMS applications

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2006年第9期91卷 977-991页

作者： Marseguerra, M. Zio, E. Martorell, S. Politecn Milan Dept Nucl Engn I-20133 Milan Italy Univ Politecn Valencia Dept Chem & Nucl Engn Valencia 46022 Spain

This paper discusses the use of genetic algorithms (GA) within the area of reliability, availability, maintainability and safety (RAMS) optimization. First, the multi-objective optimization problem is formulated in general terms and two alternative approaches to its solution are illustrated. Then, the theory behind the operation of GA is presented. The steps of the algorithm are sketched to some details for both the traditional breeding procedure as well as for more sophisticated breeding procedures. The necessity of affine transforming the fitness function, object of the optimization, is discussed in detail, together with the transformation itself. In addition, how to handle constraints by the penalization approach is illustrated. Finally, specific metrics for measuring the performance of a genetic algorithm are introduced. (C) 2005 Elsevier Ltd. All rights reserved.

关键词： algorithm optimization Maintainability Metric measures Gallium Mental status In-law Genetic algorithm (GA)

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optimization Study on Weapon-Target Assignment in Problem Based on Intuitionistic Fuzzy Marine Predator algorithm 23

Optimization Study on Weapon-Target Assignment in Problem Ba...

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Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing

作者： Qiwen Wang Guibao Song Naval Aviation University China

ISBN: (纸本)9798400709241

Marine predator algorithm (MPA) is a nature-inspired metaheuristic proposed by simulating the moving strategies of marine predators and preys. To improve the solving speed and precision of weapon target assignment problems, a novel MPA solving strategy is proposed combining the theories of intuitionistic fuzzy sets (IFSs). Firstly, a WTA optimization model is established under constrained ammunition resources aiming at minimum ammunition consumption and maximum interception rate. Secondly, an intuitionistic fuzzy charisma function is introduced to optimize the ratio between global searching and local searching. Finally, a numerical example is utilized to verify the rationality of the method and the result is further compared with particle swarm optimization, whale optimization algorithm and original MPA for superiority verification. The intuitionistic fuzzy MPA is proved to have higher convergence speed, stronger global searching power and is also more stable and could be of great use in future wars.

关键词： algorithm optimization intuitionistic fuzzy sets marine predator algorithm (MPA) weapon-target assignment

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optimization algorithm of digital media sharing platform based on blockchain technology

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SECURITY AND PRIVACY 2023年第2期6卷

作者： Wang, Lin Zibo Vocat Inst Dept Animat Art Zibo 255300 Shandong Peoples R China

In today's big data era, the number of daily data visits on the platform is an incalculable number, which poses a challenge for users' data security. This article aims to study the decentralized blockchain technology, take advantage of the immutable advantages of the blockchain and integrate it into the digital media sharing platform, so as to achieve the optimization and improvement of the algorithm. This paper proposes an algorithm optimization plan for building a digital media sharing platform, adding a user data security backstage on the platform side, combining blockchain technology to design data security maintenance algorithms, and setting relevant parameters to maximize the immutability of data and ensure user data security. The experimental results of this article show that blockchain technology can be fully applied to digital media sharing platforms, and data security can be increased to more than 90%.

关键词： algorithm optimization blockchain technology data security digital media sharing platform

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optimization of Short-Lag Spatial Coherence Imaging Method

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ARCHIVES OF ACOUSTICS 2019年第4期44卷 669-679页

作者： Domaradzki, Jakub Lewandowski, Marcin Zolek, Norbert Lewandowski, Marcin Warsaw Univ Technol Fac Elect & Informat Technol Pl Politech 1 PL-00661 Warsaw Poland Polish Acad Sci Inst Fundamental Technol Res Pawinskiego 5B PL-02106 Warsaw Poland

The computing performance optimization of the Short-Lag Spatial Coherence (SLSC) method applied to ultrasound data processing is presented. The method is based on the theory that signals from adjacent receivers are correlated, drawing on a simplified conclusion of the van Cittert-Zernike theorem. It has been proven that it can be successfully used in ultrasound data reconstruction with despeckling. Former works have shown that the SLSC method in its original form has two main drawbacks: time-consuming processing and low contrast in the area near the transceivers. In this study, we introduce a method that allows to overcome both of these drawbacks. The presented approach removes the dependency on distance (the "lag" parameter value) between signals used to calculate correlations. The approach has been tested by comparing results obtained with the original SLSC algorithm on data acquired from tissue phantoms. The modified method proposed here leads to constant complexity, thus execution time is independent of the lag parameter value, instead of the linear complexity. The presented approach increases computation speed over 10 times in comparison to the base SLSC algorithm for a typical lag parameter value. The approach also improves the output image quality in shallow areas and does not decrease quality in deeper areas.

关键词： short lag spatial coherence synthetic aperture algorithm optimization parallel processing

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Bilevel parallel genetic algorithms for optimization of large steel structures

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2001年第5期16卷 295-304页

作者： Sarma, KC Adeli, H Qwest Commun Int Inc Dublin OH 43016 USA Ohio State Univ Dept Civil & Environm Engn & Geodet Sci Columbus OH 43210 USA

This article is concerned with optimization of very large steel structures subjected to the actual constraints of the American Institute of Steel Construction ASD and LRFD specifications on high-performance multiprocessor machines using biologically inspired genetic algorithms. First, parallel fuzzy genetic algorithms (GAs) are presented for optimization of steel structures using a distributed memory Message Passing Interface (MPI) with two different schemes: the processor farming scheme and the migration scheme. Next, two bilevel parallel GAs are presented for large-scale structural optimization through judicious combination of shared memory data parallel processing using the OpenMP Application Programming Interface (API) and distributed memory message passing parallel processing using MPI. Speedup results are presented for parallel algorithms.

关键词： Genetic algorithm (GA) heart atrium septum defect Parallel Lines algorithm optimization steel structures machinery machine

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