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Speedup vs. quality: Asynchronous and cluster-based distributed adaptive genetic algorithms for ordered problems

PARALLEL COMPUTING 2021年 103卷 102755-102755页

作者： Ohira, Ryoma Islam, Md Saiful Kayesh, Humayun Griffith Univ Sch Informat & Commun Technol Southport Qld 4215 Australia

While the main motivation for Parallel genetic algorithms (PGAs) has been to improve the scalability of genetic algorithms (GAs), techniques and strategies for maintaining population diversity is an equally active research topic. Island Model genetic algorithms (IMGAs) represent one of the most mature strategies for developing PGAs in an effective and scalable manner. However, identifying how much migration and which individuals should migrate are open research problems. Meanwhile, recent developments in adaptive genetic algorithms (AGAs) have led to techniques for monitoring and maintaining population diversity in an online manner. The aim of the present work is to introduce adaptive techniques and mechanisms into PGAs in order to determine when, how much and which individuals are most suitable for migration. We present a number of adaptive PGAs that aim to maintain diversity and maximise coverage of the solution space by minimising the overlap between islands. PGAs presented in this work are empirically assessed for their abilities in scalability, ability to find good quality solutions and maintain population diversity in ordered problems. These metrics are compared to existing adaptive and parallel GAs selected from the literature for their performance. We estimated the overhead costs of monitoring diversity and communication would result in a trade off between scalability and search capabilities. Our results suggest that an asynchronous adaptive PGA has the greatest speedup potential. However, while localising adaptive populations by k-means clustering is less scalable, results indicate that the method is more effective at directing the search in order to reduce the likelihood of islands searching in the same areas of the solution space. For this reason, an adaptive PGA with clustering-based migration demonstrates greater potential in solution quality while maintaining good speedup performance.

关键词： Island model Synchronous and asynchronous migration Cluster-based migration adaptive genetic algorithm Ordered problems

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Dynamic Rapid Scheduling algorithm for Vehicle Time Sensitive Communication Based on CILP and AGA

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第11期72卷 15014-15027页

作者： Zhang, Chenyu Wen, Xiangming Li, Cong Zheng, Wei Yu, Longdan Lu, Zhaoming Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Networks Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China

Vehicle time-sensitive communication (VTSC) is an emerging paradigm where the Time-Sensitive Networking together with V2X provides connected vehicle with deterministic and low communication latency. Scheduling is a critical step to realize the determinism. Recently, as the scale and variety of VTSCs increase, the requirements of simultaneously achieve multicast supportment, short runtime, dynamic schduling ability and low resource overhead challenge the traditional TSN scheduling algorithms. This article proposes a scheduling scheme with multiple stages to achieve both efficient and rapid scheduling. This article first formulates the joint routing and scheduling problems by proposing a cluster-Integer Linear Programming (CILP). It aims to accelerate the static scheduling and realize real-time response for dynamic incremental scheduling. An improved topology pruning method and a clustering-aggregation ILP procedure are presented. Then, since the dynamic scheduling will gradually reduce the resource utilization, this article proposes an adaptive genetic algorithm (AGA) to rapidly perform the resource optimization. The crossover operator, mutation operator, adaptive probability and fitness function are designed, supporting multicast TSN flows. Finally, this article conducts extensive simulations to validate the proposed algorithms. The results show that, comparing with traditional methods, the proposed CILP method can reduce the average runtime by up to 86.7% for static scheduling and realize real-time dynamic scheduling. The proposed AGA method can reduce the resource optimization runtime by up to 70% while improving the schedulability. The overall multi-stage scheduling mechanism could satisfy the VTSC scheduling requirements.

关键词： Dynamic scheduling Routing Schedules Runtime Switches Heuristic algorithms Delays adaptive genetic algorithm clustering integer linear programming dynamic scheduling joint routing and scheduling time-sensitive communication

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Mixed Integer Programming Model Based on Data algorithms in Sustainable Supply Chain Management

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第9期15卷 344-353页

作者： Dong, Shaobin Li, Aihua Huaiyin Inst Technol Fac Business Huaian 223001 Peoples R China Huaiyin Inst Technol Fac Elect & Informat Engn Huaian 223001 Peoples R China

the deepening of globalization and increasing demands for environmental sustainability, modern supply chains are faced with increasingly complex management challenges. To reduce management costs and enhance efficiency, an experimental approach is proposed based on a Mixed Integer Programming Model, integrating heuristic algorithms with adaptive genetic algorithms. The objective is to improve both the efficiency and sustainability of supply chain management. Initially, the selection of suppliers within the supply chain is analyzed. Subsequently, heuristic algorithms and genetic algorithms are jointly employed to design, generate, and optimize initial solutions. Results indicate that during initial runs on training and validation sets, the fitness values of the research method reached as high as 99.67 and 96.77 at the 22nd and 68th iterations, respectively. Moreover, on the training set with a dataset size of 112, the accuracy of the research method was 98.56%, significantly outperforming other algorithms. With the system running five times, the time consumed for supplier selection and successful order allocation was merely 0.654s and 0.643s, respectively. In practical application analysis, when the system iterated 99 times, the research method incurred the minimum total cost of 962,700 yuan. These findings demonstrate that the research method effectively minimizes supply chain management costs while maximizing efficiency, offering practical strategies for optimizing and sustainably developing supply chain management.

关键词： Mixed Integer Programming Model sustainable supply chain management heuristic algorithm adaptive genetic algorithm

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Based on IAGA-BP neural network internal temperature prediction of solar car

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INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2023年第2-4期92卷 300-315页

作者： Zhang, Tingting Jinan Engn Polytech Dept Civil Engn 6088 East Jingshi Rd Jinan 250200 Shandong Peoples R China

The instability of the interior temperature of the solar car during winter heating leads to a lot of unnecessary energy consumption. This paper proposes to predict internal temperature based on the improved adaptive genetic algorithm-back propagation neural network (IAGA-BP). Firstly, the traditional adaptive genetic algorithm's crossover and mutation probability are improved to get the improved adaptive genetic algorithm. Then, an internal temperature prediction model based on IAGA-BP neural network is established. Finally, the results of IAGA-BP are compared with results based on the particle swarm optimisation-back propagation neural network model (PSO-BP). The experimental results show that the mean absolute and square errors of IAGA-BP temperature prediction are 0.2810 and 0.1070. IAGA-BP network has better prediction accuracy than PSO-BP network. Therefore, IAGA-BP neural network temperature prediction model can reasonably predict the temperature to achieve the purpose of energy-saving.

关键词： new energy vehicle economic and environmental protection adaptive genetic algorithm BP neural network temperature prediction

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Generalized Interval Type-II Fuzzy Rough Model-Based Feature Discretization for Mixed Pixels

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第3期31卷 845-859页

作者： Chen, Qiong Ding, Weiping Huang, Xiaomeng Wang, Hao Tsinghua Univ Inst Global Change Studies Dept Earth Syst Sci Minist EducKey Lab Earth Syst Modeling Beijing 100084 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Minist Nat Resources Key Lab Marine Sci & Numer Modeling Qingdao 266061 Peoples R China Xidian Univ Sch Cyber Engn Xian 710126 Peoples R China Norwegian Univ Sci & Technol Dept Comp Sci N-2815 Gjovik Norway

Feature discretization algorithms of remote sensing images are often based on the assumption that a sample only belongs to a single category and cannot describe uncertainty caused by mixed pixels. Fuzzy rough models quantify uncertain information by introducing the memberships of pixels to each category. However, there are large errors in the decomposition model of mixed pixels, making the obtained memberships fuzzy. To overcome this weakness, we propose a feature discretization algorithm based on the generalized interval type-II fuzzy rough set for mixed pixels (GIT2FRSD). We use the fuzzy mean vector and the fuzzy covariance matrix to calculate the primary grades of pixels to each ground object and determine the secondary grades according to the distribution of pixels in the boundary region of the rough set. Then, we construct the fitness function using the magnitude of the reduction of the number of breakpoints and the average approximation precision of the generalized interval type-II fuzzy rough set and search for the best discrete breakpoints in all bands of the remote sensing image using an adaptive genetic algorithm. Our method further fuzzifies the abundance information, more accurately quantifying and evaluating the uncertainty caused by mixed pixels at a time complexity similar to that of the fuzzy rough model. The experimental results on GF-2 and Landsat 8 images show that compared with current mainstream discretization algorithms, our method has better search efficiency. It obtains the minimum number of discrete intervals while ensuring data consistency and achieves the highest classification accuracy.

关键词： Fuzzy sets Rough sets Uncertainty Remote sensing Earth Data models Numerical models adaptive genetic algorithm boundary region feature discretization generalized interval type-II (GIT2) fuzzy rough set mixed pixels

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Burden Distribution Control Based on adaptive genetic algorithm in Bell-less Blast Furnace

Burden Distribution Control Based on Adaptive Genetic Algori...

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第三十三届中国控制会议

作者： Qiao Zhu Yi-Xin Yin Han-Qing Cheng Xian-Zhong Chen School of Automation & Electrical Engineering University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing

ISBN: (纸本)9781479947249

The proper control of the ratio of ore to coke distribution(ROCD) can achieve energy saving of the blast furnace(BF).In order to achieve the desired ROCD,it is meaningful to search best charging system(especially the burden matrix).This paper deals with the problem of burden distribution control based on the adaptive genetic algorithm and multi-radar ***,the definition of the ratio of ore to coke(ROC) and the desired ROCD are ***,according to the desired ROCD,the concrete steps of searching the best burden matrices are presented based on the adaptive genetic ***,the operators of the adaptive genetic algorithm(especially the mutation operator) are properly adjusted,according to the actual production process of the bell-less ***,three computational experiments demonstrate the effectiveness of the method.

关键词： Bell-less blast furnace adaptive genetic algorithm Ratio of ore to coke distribution Multi-loop burden matrices.

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TATune: A RocksDB Knob Tuning System Based on Transformer

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IEEE ACCESS 2023年 11卷 143589-143600页

作者： Hu, Yun-Zhang Wang, Hui Shanghai Inst Technol Sch Comp Sci & Informat Engn Shanghai 201418 Peoples R China

RocksDB is a powerful database engine that offers a wide range of adjustable knobs, which greatly influence its performance. However, configuring RocksDB manually for optimal performance is challenging due to the large number of available knobs and their complex settings. To address this issue, we propose Transformer adaptive Gentic algorithm Tune(TATune), an auto-tuning system for RocksDB knobs. In TATune, knob configuration files for RocksDB are randomly generated and executed at different preset workloads first. Subsequently, the correlation between the knob and RocksDB performance is learned by the prediction model based on Transformer. Finally, an adaptive genetic algorithm that utilizes the prediction model as a fitness function to recommend the RocksDB knob setting. Additionally, a novel optimization metric is also proposed to evaluate the performance of the auto-tuning RocksDB knob system. TATune is compared with other approaches to configure RockDB knobs on six distinct workloads. The results indicate that TATune is effective and achieves significant performance improvement across various target workloads. The final average optimization performance is 26% better than K2vTune and 72% better than RTune.

关键词： RocksDB auto-tuning knob configuration transformer adaptive genetic algorithm

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adaptive genetic algorithm for Parameter Identification of Centrifugal Compressor

Adaptive Genetic Algorithm for Parameter Identification of C...

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第25届中国控制与决策会议

作者： Wang Xiaogang Bai Xueliang Jiang Bo College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781467355339

Parameters of mechanism model of centrifugal compressor is wide-ranging and artificial selection is difficult to solve. Transforming parameter identification problem of the multistage compressor model into an optimization problem, adaptive genetic algorithm （AGA） is used to decide the unknown parameters in the model. Model verification results show that the parameters identification can reflect the operating characteristics of centrifugal compressors and the precision of the model is improved.

关键词： Centrifugal compressor Mechanism model Parameter identification adaptive genetic algorithm

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Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 2073-2083页

作者： Ma, Changxi He, Ruichun Lanzhou Jiaotong Univ Sch Traff & Transportat Anning West Rd Lanzhou 730070 Gansu Peoples R China

The urban green wave traffic control system is one of the effective means to solve the problems of urban traffic jams and reduce vehicle delay. Aiming at the problems of traditional green wave traffic control in optimization computation, this paper introduces the adaptive mechanism and crossover, mutation operators to artificial fish swarm algorithm (AFSA) in order to adjust the evolution group. Through setting bulletin board measure and retention strategy, the individual status of the optimal artificial fish is recorded. In that way, the adaptive genetic AFSA is established. Finally, the paper applies this algorithm to green wave traffic control in five continuous intersections on Jianning Road, Lanzhou, China, gains one traffic control program better than traditional method through calculation, and verifies the feasibility and effectiveness of adaptive genetic-AFSA to optimize green wave traffic control system.

关键词： adaptive genetic algorithm Artificial fish swarm algorithm Green wave traffic Control system Optimization

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A Novel Proposal in Multilevel Inverter with genetic algorithm Optimization

A Novel Proposal in Multilevel Inverter with Genetic Algorit...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Choudry, V. Bharath Kavithamani, A. Karthik, S. Sam Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering Tamilnadu Coimbatore India Department of Electrical and Electronics Engineering Coimbatore Institute of Technology Tamilnadu Coimbatore India

ISBN: (纸本)9798350325188

In transformation of resources like power the multilevel inverters are the best technology. Output as stair waveform signal feed as input to many of the DC sources. The description of multilevel inverter topology is described in the paper. The presentation consist of 7 level architecture principle and to obtain best quality of output signal the algorithm of adaptive genetic is adopted to slow down the harmonic content. In actual time the evaluations of appropriate switching angle used to analyse the THD minimization. The overall observations are declined based on 7 level to cases in other levels and THD determination in various states. These studies are based on MATLAB simulation and programming. The proposed work is simulated using MATLAB software which gives the model accuracy rate of 80% with the lesser error rate. The multilevel inverter designed using the proposed method removes the harmonic function at various input voltage with the minimum loss factors. © 2023 IEEE.

关键词： adaptive genetic algorithm harmonic function multilevel inverters THD minimization

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