• Title/Summary/Keyword: Optimization

Search Result 20,384, Processing Time 0.098 seconds

A Hybridization of Adaptive Genetic Algorithm and Particle Swarm Optimization for Numerical Optimization Functions

  • Yun, Young-Su;Gen, Mitsuo
    • Proceedings of the Korea Society for Industrial Systems Conference
    • /
    • 2008.10b
    • /
    • pp.463-467
    • /
    • 2008
  • Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

  • PDF

Structural Optimization Study about Support Structure of Pressure Container (압력용기 지지구조물의 구조최적화 연구)

  • Kim, Chang-Sik
    • Journal of the Korea Institute of Military Science and Technology
    • /
    • v.8 no.2 s.21
    • /
    • pp.22-29
    • /
    • 2005
  • In this study we performed topology optimization and size optimization about support structure of pressure container which is installed in a Common Bed. The optimization study shows that structure weight optimization results can be applied to navy ship. The topology optimization is performed by static load, homogenization and optimality criteria method and size optimization is performed by SOL200 of NASTRAN.

Visualization Tool Design for Searching Process of Particle Swarm Optimization (Particle Swarm Optimization 탐색과정의 가시화를 위한 툴 설계)

  • 유명련
    • Journal of Korea Multimedia Society
    • /
    • v.6 no.2
    • /
    • pp.332-339
    • /
    • 2003
  • To solve the large scale optimization problem approximately, various approaches have been introduced. Recently the Particle Swarm Optimization has been introduced. The Particle Swarm Optimization simulates the process of birds flocking or fish schooling for food, as with the information of each agent is skated by other agents. The Particle Swarm Optimization technique has been applied to various optimization problems whose variables are continuous. However, there are seldom trials for visualization of searching process. This paper proposes a new visualization tool for searching process of Particle Swarm Optimization algorithm. The proposed tool is effective for understanding the searching process of Particle Swarm Optimization method and educational for students. The computational results can be shown tiny and very helpful for education.

  • PDF

A teaching learning based optimization for truss structures with frequency constraints

  • Dede, Tayfun;Togan, Vedat
    • Structural Engineering and Mechanics
    • /
    • v.53 no.4
    • /
    • pp.833-845
    • /
    • 2015
  • Natural frequencies of the structural systems should be far away from the excitation frequency in order to avoid or reduce the destructive effects of dynamic loads on structures. To accomplish this goal, a structural optimization on size and shape has been performed considering frequency constraints. Such an optimization problem has highly nonlinear property. Thus, the quality of the solution is not independent of the optimization technique to be applied. This study presents the performance evaluation of the recently proposed meta-heuristic algorithm called Teaching Learning Based Optimization (TLBO) as an optimization engine in the weight optimization of the truss structures under frequency constraints. Some examples regarding the optimization of trusses on shape and size with frequency constraints are solved. Also, the results obtained are tabulated for comparison. The results demonstrated that the performance of the TLBO is satisfactory. Additionally, TLBO is better than other methods in some cases.

Structural Analysis and Dynamic Design Optimization of a High Speed Multi-head Router Machine (다두 Router Machine 구조물의 경량 고강성화 최적설계)

  • 최영휴;장성현;하종식;조용주
    • Proceedings of the Korean Society of Precision Engineering Conference
    • /
    • 2004.10a
    • /
    • pp.902-907
    • /
    • 2004
  • In this paper, a multi-step optimization using a G.A. (Genetic Algorithm) with variable penalty function is introduced to the structural design optimization of a 5-head route machine. Our design procedure consist of two design optimization stage. The first stage of the design optimization is static design optimization. The following stage is dynamic design optimization stage. In the static optimization stage, the static compliance and weight of the structure are minimized simultaneously under some dimensional constraints and deflection limits. On the other hand, the dynamic compliance and the weight of the machine structure are minimized simultaneously in the dynamic design optimization stage. As the results, dynamic compliance of the 5-head router machine was decreased by about 37% and the weight of the structure was decreased by 4.48% respectively compared with the simplified structure model.

  • PDF

Study of Hybrid Optimization Technique for Grain Optimum Design

  • Oh, Seok-Hwan;Kim, Yong-Chan;Cha, Seung-Won;Roh, Tae-Seong
    • International Journal of Aeronautical and Space Sciences
    • /
    • v.18 no.4
    • /
    • pp.780-787
    • /
    • 2017
  • The propellant grain configuration is a design variable that determines the shape and performance of a solid rocket motor. Grain configuration variables have complicated effects on the motor performance; so the global optimization problem has to be solved in order to design the configuration variables. The grain performance has been analyzed by means of the grain burn-back and internal ballistic analysis, and the optimization technique searches for the configuration variables that satisfy the requirements. The deterministic and stochastic optimization techniques have been applied for the grain optimization, but the results are imperfect. In this study, the optimization design of the configuration variables has been performed using the hybrid optimization technique, which combines those two techniques. As a result, the hybrid optimization technique has proved to be efficient for the grain optimization design.

A Study on Design Optimization of Mooring Pier using Prestressed Precast Concrete Panel (프리스트레스트 프리캐스트 콘크리트 패널을 이용한 잔교식부두의 최적설계)

  • 조병완;태기호;김용철
    • Proceedings of the Korea Concrete Institute Conference
    • /
    • 2000.10a
    • /
    • pp.253-258
    • /
    • 2000
  • Recently, the area of design optimization, especially structural optimization, has been and to be a continuous active area of research. And the design optimizations of port facilities have been achieved by many other civil engineers. But the design optimization of port facilities were limited to the design optimization of the breasting dolphin. This paper invested the design optimization of mooring pier and the foundations of mooring pier was suggested considering the convenience of repair and reinforcement work. The mooring pier devised with prestressed precast concrete panel and rigid frame welded wide flange beam to steel pipe pile. To accomplish the design optimization of mooring pier, the Augmented Lagrangian Multiplier Method(ALM) of ADS(Garret N. Vanderplaats) optimization routine, BFGS method as optimizer and Golden Section Method as one dimensional search were utilized. As a result, thirty percent of material cost for construction was reduced by design optimization. The tensile stress of concrete panel and bottom flage was critical constraints under service load. So, using high strength concrete and steel will be economical. And lots of initial values must be invested to accomplish the design optimization in design procedures.

  • PDF

Hull Form Optimization using Parametric Modification Functions and Global Optimization (전역 최적화기법과 파라메트릭 변환함수를 이용한 선형 최적화)

  • Kim, Hee-Jung;Chun, Ho-Hwan;An, Nam-Hyun
    • Journal of the Society of Naval Architects of Korea
    • /
    • v.45 no.6
    • /
    • pp.590-600
    • /
    • 2008
  • This paper concerns the development of a designer friendly hull form parameterization and its coupling with advanced global optimization algorithms. As optimization algorithms, we choose the Partial Swarm Optimization(PSO) recently introduced to solve global optimization problems. Most general-purpose optimization softwares used in industrial applications use gradient-based algorithms, mainly due to their convergence properties and computational efficiency when a relatively few number of variables are considered. However, local optimizers have difficulties with local minima and non-connected feasible regions. Because of the increase of computer power and of the development of efficient Global Optimization (GO) methods, in recent years nongradient-based algorithms have attracted much attention. Furthermore, GO methods provide several advantages over local approaches. In the paper, the derivative-based SQP and the GO approach PSO are compared with their relative performances in solving some typical ship design optimization problem focusing on their effectiveness and efficiency.

A cross-entropy algorithm based on Quasi-Monte Carlo estimation and its application in hull form optimization

  • Liu, Xin;Zhang, Heng;Liu, Qiang;Dong, Suzhen;Xiao, Changshi
    • International Journal of Naval Architecture and Ocean Engineering
    • /
    • v.13 no.1
    • /
    • pp.115-125
    • /
    • 2021
  • Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the "curse of dimensionality" when dealing with HEB problems. A recently proposed Cross-Entropy (CE) optimization algorithm is an advanced stochastic optimization algorithm based on a probability model, which has the potential to deal with high-dimensional optimization problems. Currently, the CE algorithm is still in the theoretical research stage and rarely applied to actual engineering optimization. One reason is that the Monte Carlo (MC) method is used to estimate the high-dimensional integrals in parameter update, leading to a large sample size. This paper proposes an improved CE algorithm based on quasi-Monte Carlo (QMC) estimation using high-dimensional truncated Sobol subsequence, referred to as the QMC-CE algorithm. The optimization performance of the proposed algorithm is better than that of the original CE algorithm. With a set of identical control parameters, the tests on six standard test functions and a hull form optimization problem show that the proposed algorithm not only has faster convergence but can also apply to complex simulation optimization problems.