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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Heien, Eric M. Fujimoto, Noriyuki Hiroyasu, Tomoyuki Osaka Univ Grad Sch Informat Sci & Technol Toyonaka Osaka 5608531 Japan Doshisha Univ Dept Knowledge Engn Kyoto 6100321 Japan

ISBN: (纸本)9781595936974

Although BOA is effective at finding solutions for optimization problems, small population sizes in a model can result in premature convergence to a sub-optimal solution. One way of avoiding premature convergence is to increase population diversity with a mutation operator. In our experiments, we compare several mutation operators for use with BOA. We examine in detail the probabilistic model utilizing (PMU) bit flipping mutation operator. We compare the effectiveness of the PMU operator with standard BOA, self-adaptive evolution and local search of substructural neighborhoods.

关键词： bayesian optimization algorithm mutation probabilistic model building genetic algorithm

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Study on the Performance of Decision Graph bayesian optimization algorithm

Study on the Performance of Decision Graph Bayesian Optimiza...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Shi, Zhi-fu Liu, Hai-yan Zhu, Yin-sheng Xian Inst Appl Opt Res Lab 4 Xian Peoples R China 2 Artillery Engn Coll Res Lab 603 Xian Peoples R China

ISBN: (纸本)9780769538167

The evolutions computation is the best proceeding algorithm for all kinds of optimization problem In the world. bayesian optimization algorithm (BOA) is one kind of the evolution algorithm which is advantage on others for high order, hierarchical and correlative on anther optimization problem. For improving the ability of the BOA, the decision graph was introduced to enhance the represent and learn of bayesian network and compress the parameter saving. The optimization mechanism and the algorithm model were studied in detail. The evaluation indexes and test function were also built for validating the merits. The performance and efficiency of DBOA were analyzed with compare with BOA, basic genetic algorithm and binary particle swarm optimization. The test results showed that DBOA was effective for hierarchical decomposable function.

关键词： bayesian optimization algorithm decision graph hierarchical decomposable function performance analysis

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Performance optimization of Web Front-end Frameworks: Automatic Adjustment Strategies Based on bayesian optimization algorithm 24

Performance Optimization of Web Front-end Frameworks: Automa...

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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Chen, Cuiqin Meng, Qing Huang, Junze Hainan Vocat Univ Haikou 570216 Hainan Peoples R China

ISBN: (纸本)9798400718144

In modern web development, performance optimization of front-end frameworks has become a key issue in improving user experience and system efficiency. The existing manual adjustment methods are often time-consuming and have unstable effects. This article proposes an automatic adjustment strategy based on bayesian optimization algorithm to achieve efficient optimization of front-end framework performance. After using bayesian optimization algorithm, the average time of Largest Contentful Paint (LCP) is reduced to 2052 milliseconds. In terms of response speed indicators, the optimized time to interactive (TTI) decreases to 2923 milliseconds. Users are also very satisfied with the optimized experience. After using bayesian optimization algorithm, the CPU (Central Processing Unit) utilization rate decreases to 65.9% and the memory usage decreases to 324.4MB. From the data conclusion, it can be seen that the automatic adjustment strategy based on bayesian optimization algorithm has shown significant advantages in improving the performance, response speed, user experience, and resource utilization efficiency of web front-end frameworks.

关键词： Web front-end Front-end framework bayesian optimization algorithm Performance optimization Heuristic algorithm

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Combinatorial Effects of Local Structures and Scoring Metrics in bayesian optimization algorithm 09

Combinatorial Effects of Local Structures and Scoring Metric...

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World Summit on Genetic and Evolutionary Computation (GEC 09)

作者： Karshenas, Hossein Nikanjam, Amin Helmi, B. Hoda Rahmani, Adel T. Iran Univ Sci & Technol Tehran Iran

ISBN: (纸本)9781605583266

bayesian optimization algorithm (BOA) has been used with different local structures to represent more complex models and a variety of scoring metrics to evaluate bayesian network. But the combinatorial effects of these elements on the performance of BOA have not been investigated yet. In this paper the performance of BOA is studied using two criteria: Number of fitness evaluations and structural accuracy of the model. It is shown that simple exact local structures like CPT in conjunction with complexity penalizing BIC metric outperforms others in terms of model accuracy. But considering number of fitness evaluations (efficiency) of the algorithm, CPT with other complexity penalizing metric K2P performs better.

关键词： Estimation of Distribution algorithms bayesian optimization algorithm Scoring Metrics Local Structures

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Using and Interpreting the bayesian optimization algorithm to Improve Early Stage Design of Marine Structures

Using and Interpreting the Bayesian Optimization Algorithm t...

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作者： Devine, Thomas E. University of Michigan

学位级别：Ph.D.

Early stage naval structural design continues to advance as designers seek to improve the quality and speed of the design process. The early stages of design produce preliminary dimensions or scantlings which control the cost and structural performance of a vessel. Increased complexity in the evaluation of structural response has led to a need for efficient algorithms well suited to solving structural design specific optimization problems. As problem sizes increase, existing optimizers can become slow or inaccurate. The bayesian optimization algorithm (BOA) is presented as one solution to efficiently solve problems in the structural design optimization process. The bayesian optimization algorithm is an Estimation of Distribution algorithm (EDA) that uses a statistical sample of potential design solutions to create and train a bayesian network (BN). The application of BNs is well suited for nearly decomposable problem composition which closely matches rules based structural design evaluation. This makes the BOA well suited to solve complex early stage structural optimization problems. Additionally, the learning processes used to create and train the BNs can be analyzed and interpreted to capture design knowledge. This return of knowledge to the designer helps to improve designer intuition and model synthesis in the face of more complex and intricate models. The BNs are thus analyzed to augment design problem understanding and explore trade-offs within the design space. The result matches a paradigm shift in early stage optimization of naval structures. Designers gain better understanding of critical design variables and their interactions as compared to the previous focus on the single most optimal solution. This leads to efficient simulations which rapidly explore design spaces, document critical design variable relationships and enable the designer to create better early stage design solutions.

关键词： Articleral Design bayesian optimization algorithm Design optimization Naval Architecture Thesis

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A bayesian optimization algorithm for UAV path planning

A bayesian optimization algorithm for UAV path planning

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2nd International Conference on Intelligent Information Processing

作者： Fu, X Gao, X Chen, D Northwestern Polytech Univ Xian 710072 Peoples R China

ISBN: (纸本)038723151X

A bayesian optimization algorithm (BOA) for unmanned aerial vehicle (UAV) path planning is presented, which involves choosing path representation and designing appropriate metric to measure the quality of the constructed network. Unlike our previous work in which genetic algorithm (GA) was used to implement implicit teaming, the teaming in the proposed algorithm is explicit, and the BOA is applied to implement such explicit teaming by building a bayesian network of the joint distribution of solutions. Experimental results demonstrate that this approach can overcome some drawbacks of other path planning algorithms. It is also suggested that the teaming mechanism in the proposed approach might be suitable for other multivariate encoding problems.

关键词： UAV path planning bayesian network genetic algorithm bayesian optimization algorithm

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A stochastic framework using bayesian optimization algorithm to assess the network-level societal impacts of connected and autonomous vehicles

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2022年第0期139卷

作者： Fakhrmoosavi, Fatemeh Kamjoo, Ehsan Kavianipour, Mohammadreza Zockaie, Ali Talebpour, Alireza Mittal, Archak Michigan State Univ E Lansing MI 48824 USA Univ Illinois Urbana IL USA Ford Motor Co Dearborn MI 48121 USA

Connected and autonomous vehicle technologies are expected to alter transportation systems by enhancements in mobility, safety, and emission reduction. While many studies have investigated the impacts of connected vehicle (CV) and autonomous vehicle (AV) technologies on traffic congestion and emission at the facility level, little is known about these impacts at large scales. Furthermore, different effective parameters associated with these impacts, such as the extra vehicle miles traveled (VMT) induced by AVs, technology cost of these vehicles, and possible reductions in the value of time of AV users, are highly uncertain. The uncertainty of these parameters and its role in assessing the societal impacts of CVs and AVs are not fully explored in the literature. Therefore, this study aims to develop a stochastic framework and an optimization algorithm to find the optimum market shares of CVs and AVs in a mixed traffic environment, consisting of human-driven vehicles without connectivity (HDVs), CVs, and AVs, minimizing the system cost. Emission, travel time, and technology costs are considered as components of the system cost. Thus, the framework combines a traffic simulation tool that considers a mixed fleet of HDVs, CVs, and AVs with heterogeneous drivers (for HDVs and CVs) distributed spatially over the network, along with an emission estimation model, to measure network-wide travel time and emission costs. Many parameters, such as extra VMT produced by AVs, value of time reduction for AV users, and automation cost, are subject to a considerable degree of stochasticity, which is considered by assuming probabilistic distributions for these parameters. A bayesian optimization algorithm with heteroskedastic non-stationary Gaussian process model is presented to estimate the optimum market shares of CVs and AVs considering these uncertainties. The stochastic framework and the optimization algorithm are successfully applied to a large-scale network of Chicago. The impac

关键词： Network Modeling bayesian optimization algorithm Connected and Autonomous Vehicles Stochastic optimization Traffic Simulation

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EVOLUTIONARY optimization IN DYNAMIC ENVIRONMENTS: BRINGING THE STRENGTHS OF DYNAMIC bayesian NETWORKS INTO bayesian optimization algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第6期9卷 2485-2503页

作者： Kaedi, Marjan Ghasem-Aghaee, Nasser Ahn, Chang Wook Univ Isfahan Dept Informat Technol Engn Esfahan *** Iran Univ Isfahan Dept Comp Engn Esfahan *** Iran Sungkyunkwan Univ Dept Comp Engn 2066 Seobu Ro Suwon 440746 South Korea

In this paper, a new evolutionary algorithm termed DBN-MBOA (Memory based BOA with Dynamic bayesian Networks) is proposed for the dynamic optimization. In DBN-MBOA, the knowledge obtained from previously solved problems is encoded in some structures called network translators. The network translators defined on non-stationary Dynamic bayesian Networks (nsDBNs) describe the correlation between conditional dependencies of candidate solution variables before and after environmental changes. The network translators constructed for the changes are stored in memory. When any change occurs in the environment, a relevant network translator is retrieved from the memory and is used for modifying the dependencies of the current bayesian network. In the retrieve stage, unlike existing memory-based methods, the relevant network translator is selected based on the characteristic of the change itself, not that of the new environmental state. Experimental results show that DBN-MBOA achieves better performance in random environments as well as cyclic environments.

关键词： bayesian optimization algorithm Dynamic bayesian network Dynamic optimization Environmental change Memory-based method Network translator

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AN IMPROVED REAL-CODED bayesian optimization algorithm FOR CONTINUOUS GLOBAL optimization

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第6期9卷 2505-2519页

作者： Moradabadi, Behnaz Beigy, Hamid Ahn, Chang Wook Sharif Univ Technol Dept Comp Engn Tehran Iran Sungkyunkwan Univ Dept Comp Engn 2066 Seobu Ro Suwon 440746 South Korea

bayesian optimization algorithm (BOA) utilizes a bayesian network to estimate the probability distribution of candidate solutions and creates the next generation by sampling the constructed bayesian network. This paper proposes an improved real-coded BOA (IrBOA) for continuous global optimization. In order to create a set of bayesian networks, the candidate solutions are partitioned by an adaptive clustering method. Each bayesian network has its own structure and parameters, and the next generation is produced from this set of networks. The adaptive clustering method automatically determines the correct number of clusters so that the probabilistic building-block crossover (PBBC) is effectively preserved. This leads to a better search when the diversity of population is high at the beginning of search. Moreover, it tunes the solutions by automatically decreasing the number of clusters as the diversity of population decreases during the search process. The experimental results demonstrate that the proposed algorithm achieves better performance on well-known benchmark functions in the continuous global optimization.

关键词： Evolutionary algorithms bayesian optimization algorithm Adaptive clustering method Probabilistic building-block crossover Continuous global optimization

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The Path Planning of Standoff Land Missile in Low Altitude Penetration Based on bayesian optimization algorithm

The Path Planning of Standoff Land Missile in Low Altitude P...

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智能计算及其应用国际会议

作者： SHI Zhifu,ZHANG An , ZHANG Yongfang (Department of System & Control Engineering, School of Electronic & Information , Northwest Polytechnic University , Xi’an 710072, China)

Standoff land missile has started the road of far distance, beyond low altitude and high accuracy. Every step of the planning will affect the following steps in the path planning. The existing methods

关键词： bayesian optimization algorithm standoff land missile path planning low altitude penetration

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