• Title/Summary/Keyword: performance-based optimization

Search Result 2,574, Processing Time 0.026 seconds

Pareto fronts-driven Multi-Objective Cuckoo Search for 5G Network Optimization

  • Wang, Junyan
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.14 no.7
    • /
    • pp.2800-2814
    • /
    • 2020
  • 5G network optimization problem is a challenging optimization problem in the practical engineering applications. In this paper, to tackle this issue, Pareto fronts-driven Multi-Objective Cuckoo Search (PMOCS) is proposed based on Cuckoo Search. Firstly, the original global search manner is upgraded to a new form, which is aimed to strengthening the convergence. Then, the original local search manner is modified to highlight the diversity. To test the overall performance of PMOCS, PMOCS is test on three test suits against several classical comparison methods. Experimental results demonstrate that PMOCS exhibits outstanding performance. Further experiments on the 5G network optimization problem indicates that PMOCS is promising compared with other methods.

A Study on the Ranked Bidirectional Evolutionary Structural Optimization (등급 양방향 진화적 구조 최적화에 관한 연구)

  • Lee, Yeong-Sin;Ryu, Chung-Hyeon;Myeong, Chang-Mun
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.25 no.9
    • /
    • pp.1444-1451
    • /
    • 2001
  • The evolutionary structural optimization(ESO) method has been under continuous development since 1992. The bidirectional evolutionary structural optimization(BESO) method is made of additive and removal procedure. The BESO method is very useful to search the global optimum and to reduce the computational time. This paper presents the ranked bidirectional evolutionary structural optimization(R-BESO) method which adds elements based on a rank, and the performance indicator which can estimate a fully stressed model. The R-BESO method can obtain the optimum design using less iteration number than iteration number of the BESO.

Aerodynamic optimization of twisted tall buildings

  • Magdy Alanani;Ahmed Elshaer;Girma Bitsuamlak
    • Wind and Structures
    • /
    • v.39 no.2
    • /
    • pp.101-110
    • /
    • 2024
  • Tall buildings are distinguished by their slenderness, making them sensitive to wind loads. A huge amount of resources is typically dedicated to controlling loads and vibrations caused by wind. Enhancing tall buildings' aerodynamic performance can save a large portion of these expenses. This enhancement can be achieved through aerodynamic optimization that can be tackled either by altering the outer shape of the building locally through modifying the corners (e.g., corner chamfering) or globally through changing the whole form of the building (e.g., twisting). In this paper, a newly developed aerodynamic optimization procedure (AOP) is adopted to enhance tall buildings' aerodynamic performance. This procedure is a combination of computational fluid dynamics (CFD), Artificial Neural Networks (ANN) and Genetic algorithm (GA). An ANN-based surrogate model is used to evaluate the aerodynamic parameters through the optimization procedure to reach a reliable aerodynamic shape. Helical twisting and corner modifications of the buildings are used to reduce the along-wind base moment.

A Framework to Automate Reliability-based Structural Optimization based on Visual Programming and OpenSees

  • Lin, Jia-Rui;Xiao, Jian;Zhang, Yi
    • International conference on construction engineering and project management
    • /
    • 2020.12a
    • /
    • pp.225-234
    • /
    • 2020
  • Reliability-based structural optimization usually requires designers or engineers model different designs manually, which is considered very time consuming and all possibilities cannot be fully explored. Otherwise, a lot of time are needed for designers or engineers to learn mathematical modeling and programming skills. Therefore, a framework that integrates generative design, structural simulation and reliability theory is proposed. With the proposed framework, various designs are generated based on a set of rules and parameters defined based on visual programming, and their structural performance are simulated by OpenSees. Then, reliability of each design is evaluated based on the simulation results, and an optimal design can be found. The proposed framework and prototype are tested in the optimization of a steel frame structure, and results illustrate that generative design based on visual programming is user friendly and different design possibilities can be explored in an efficient way. It is also reported that structural reliability can be assessed in an automatic way by integrating Dynamo and OpenSees. This research contributes to the body of knowledge by providing a novel framework for automatic reliability evaluation and structural optimization.

  • PDF

A Cross Layer Optimization Technique for Improving Performance of MLC NAND Flash-Based Storages (MLC 낸드 플래시 기반 저장장치의 쓰기 성능 개선을 위한 계층 교차적 최적화 기법)

  • Park, Jisung;Lee, Sungjin;Kim, Jihong
    • Journal of KIISE
    • /
    • v.44 no.11
    • /
    • pp.1130-1137
    • /
    • 2017
  • The multi-leveling technique that stores multiple bits in a single memory cell has significantly improved the density of NAND flash memory along with shrinking processes. However, because of the side effects of the multi-leveling technique, the average write performance of MLC NAND flash memory is degraded more than twice that of SLC NAND flash memory. In this paper, we introduce existing cross-layer optimization techniques proposed to improve the performance of MLC NAND flash-based storages, and propose a new integration technique that overcomes the limitations of existing techniques by exploiting their complementarity. By fully exploiting the performance asymmetry in MLC NAND flash devices at the flash translation layer, the proposed technique can handle many write requests with the performance of SLC NAND flash devices, thus significantly improving the performance of NAND flash-based storages. Experimental results show that the proposed technique improves performance 39% on average over individual techniques.

Design of PID Controller for Magnetic Levitation RGV Using Genetic Algorithm Based on Clonal Selection (클론선택기반 유전자 알고리즘을 이용한 자기부상 RGV의 PID 제어기 설계)

  • Cho, Jae-Hoon;Kim, Yong-Tae
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.22 no.2
    • /
    • pp.239-245
    • /
    • 2012
  • This paper proposes a novel optimum design method for the PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV) by a genetic algorithm using clone selection method and a new performance index function with performances of both time and frequency domain. Generally, since an attraction type levitation system is intrinsically unstable and requires a delicate controller that is designed considering overshoot and settling time, it is difficult to completely satisfy the desired performance through the methods designed by conventional performance indexes. In the paper, the conventional performance indexes are analyzed and then a new performance index for Maglev-based RGV is proposed. Also, an advanced genetic algorithm which is designed using clonal selection algorithm for performance improvement is proposed. To verify the proposed algorithm and the performance index, we compare the proposed method with a simple genetic algorithm and particle swarm optimization. The simulation results show that the proposed method is more effective than conventional optimization methods.

Impact Performance Optimization of Auto-Sensing Breaker using Multi-objective Function (다목적함수를 이용한 지능형 브레이커의 타격성능 최적화)

  • Lee, Dae-Hee;Noh, Dae-Kyung;Park, Sung-Su;Lee, Geun-Ho;Kang, Young-Ky;Cho, Jae-Sang;Jang, Joo-Sup
    • Journal of the Korea Society for Simulation
    • /
    • v.26 no.4
    • /
    • pp.11-21
    • /
    • 2017
  • This paper discusses the design parameter sensitivity analysis and multi-objective function optimization for improving the impact performance of an auto-sensing breaker based on the analytical model of the same, which secured reliability in a previous research. The study aims to improve both impact power and stability by complementing the existing research that only improved the impact power. The study sequence is as follows: first, the analysis scenarios for the accurate sensitivity analysis and optimization are set up. Second, the sensitivity of the design parameter of the auto-sensing breaker is analyzed, and the variables with high sensitivity are extracted. Third, the extracted variables are used to optimize the multi-objective functions, and the optimized performance is compared with the initial performance to see how the impact performance on the existing auto-sensing breaker has improved. This study is based on domestic technology, and will allow the development of products with a better blowing performance than their existing overseas counterparts.

Enhancement of Rejection Performance using the PSO-NCM in Noisy Environment (잡음 환경하에서의 PSO-NCM을 이용한 거절기능 성능 향상)

  • Kim, Byoung-Don;Song, Min-Gyu;Choi, Seung-Ho;Kim, Jin-Young
    • Speech Sciences
    • /
    • v.15 no.4
    • /
    • pp.85-96
    • /
    • 2008
  • Automatic speech recognition has severe performance degradation under noisy environments. To cope with the noise problem, many methods have been proposed. Most of them focused on noise-robust features or model adaptation. However, researchers have overlooked utterance verification (UV) under noisy environments. In this paper we discuss UV problems based on the normalized confidence measure. First, we show that UV performance is also degraded in noisy environments with the experiments of an isolated word recognition. Then we observe how the degradation of UV performances is suffered. Based on the UV experiments we propose a modeling method of the statistics of phone confidences using sigmoid functions. For obtaining the parameters of the sigmoidal models, the particle swarm optimization (PSO) is adopted. The proposed method improves 20% rejection performance. Our experimental results show that the PSO-NCM can apply noise speech recognition successfully.

  • PDF

Study of nonlinear hysteretic modelling and performance evaluation for piezoelectric actuators based on activation functions

  • Xingyang Xie;Yuguo Cui;Yang Yu
    • Smart Structures and Systems
    • /
    • v.33 no.2
    • /
    • pp.133-143
    • /
    • 2024
  • Piezoelectric (PZT) actuators have been widely used in precision positioning fields for their excellent displacement resolution. However, due to the inherent characteristics of piezoelectric actuators, hysteresis has been proven to greatly reduce positioning performance. In this paper, five mathematical hysteretic models based on activation function are proposed to characterize the nonlinear hysteresis characteristics of piezoelectric actuators. Then the performance of the proposed models is verified by particle swarm optimization (PSO) algorithm and the experiment data. Thirdly, the fitting performance of the proposed models is compared with the classical Bouc-Wen model. Finally, the performance of the five proposed models in modelling hysteresis nonlinearity of piezoelectric drivers is compared, in terms of RMSE, MAPE, SAPE and operation efficiency, and relevant suggestions are given.

Comparative Study on Reliability-Based Topology Optimization (신뢰성 기반 위상최적화에 대한 비교 연구)

  • Cho, Kang-Hee;Hwang, Seung-Min;Park, Jae-Yong;Han, Seog-Young
    • Journal of the Korean Society of Manufacturing Technology Engineers
    • /
    • v.20 no.4
    • /
    • pp.412-418
    • /
    • 2011
  • Reliability-based Topology optimization(RBTO) is to get an optimal design satisfying uncertainties of design variables. Although RBTO based on homogenization and density distribution method has been done, RBTO based on BESO has not been reported yet. This study presents a reliability-based topology optimization(RBTO) using bi-directional evolutionary structural optimization(BESO). Topology optimization is formulated as volume minimization problem with probabilistic displacement constraint. Young's modulus, external load and thickness are considered as uncertain variables. In order to compute reliability index, four methods, i.e., RIA, PMA, SLSV and ADL(adaptive-loop), are used. Reliability-based topology optimization design process is conducted to obtain optimal topology satisfying allowable displacement and target reliability index with the above four methods, and then each result is compared with respect to numerical stability and computing time. The results of this study show that the RBTO based on BESO using the four methods can effectively be applied for topology optimization. And it was confirmed that DLSV and ADL had better numerical efficiency than SLSV. ADL and SLSV had better time cost than DLSV. Consequently, ADL method showed the best time efficiency and good numerical stability.