• Title, Summary, Keyword: discrete optimization

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Design Optimization of Deep Groove Ball Bearing with Discrete Variables for High-Load Capacity (이산 설계변수를 포함하고 있는 깊은 홈 볼 베어링의 고부하용량 설계)

  • Yun, Gi-Chan;Jo, Yeong-Seok;Choe, Dong-Hun
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.24 no.8
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    • pp.1940-1948
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    • 2000
  • A design method for maximizing fatigue life of the deep groove ball bearing without enlarging mounting space is proposed by using a genetic algorithm. The use of gradient-based optimization methods for the design of the bearing is restricted because this design problem is characterized by the presence of discrete design variables such as the number of balls and standard ball diameter. Therefore, the design problem of rolling element bearings is a constrained discrete optimization problem. A genetic algorithm using real coding is used to efficiently find the optimum discrete design values. To effectively deal with the design constraints, a ranking method is suggested for constructing a fitness function in the genetic algorithm. Constrains for manufacturing are applied in optimization scheme. Results obtained for several 63 series deep groove ball bearings demonstrated the effectiveness of the proposed design methodology by showing that the average basic dynamic capacities of optimally designed bearings increased about 9-34% compared with the standard ones.

Network Selection Algorithm for Heterogeneous Wireless Networks Based on Multi-Objective Discrete Particle Swarm Optimization

  • Zhang, Wenzhu;Kwak, Kyung-Sup;Feng, Chengxiao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.7
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    • pp.1802-1814
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    • 2012
  • In order to guide users to select the most optimal access network in heterogeneous wireless networks, a network selection algorithm is proposed which is designed based on multi-objective discrete particle swarm optimization (Multi-Objective Discrete Particle Swarm Optimization, MODPSO). The proposed algorithm keeps fast convergence speed and strong adaptability features of the particle swarm optimization. In addition, it updates an elite set to achieve multi-objective decision-making. Meanwhile, a mutation operator is adopted to make the algorithm converge to the global optimal. Simulation results show that compared to the single-objective algorithm, the proposed algorithm can obtain the optimal combination performance and take into account both the network state and the user preferences.

Optimum Design of Counterforted Wall Using Mixed Discrete Optimization Method (혼합이산형최적화기법을 이용한 뒷부벽식 옹벽의 최적설계)

  • Lee, Seo-Young;Kim, Jong-Ok
    • Proceedings of the Korean Society of Agricultural Engineers Conference
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    • pp.129-135
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    • 2001
  • The optimum design problems for the design of counterforted wall were formulated and computer programing to solve these problems were developed in this study. Both discrete optimization and continuous optimization method were applied to the design of counterforted wall and the results of these optimization methods were compared each other.

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Shape & Topology Optimum Design of Truss Structures Using Genetic Algorithms (유전자 알고리즘에 의한 평면 및 입체 트러스의 형상 및 위상최적설계)

  • Yuh, Baeg-Youh;Park, Choon-Wook;Kang, Moon-Myung
    • Journal of the Korean Association for Spatial Structures
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    • v.2 no.3
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    • pp.93-102
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    • 2002
  • The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

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Size, Shape and Topology Optimum Design of Trusses Using Shape & Topology Genetic Algorithms (Shape & Topology GAs에 의한 트러스의 단면, 형상 및 위상최적설계)

  • Park, Choon-Wook;Yuh, Baeg-Youh;Kim, Su-Won
    • 한국공간정보시스템학회:학술대회논문집
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    • pp.43-52
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    • 2004
  • The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algerian was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

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Optimal Design of Composite Laminated Plates with the Discreteness in Ply Angles and Uncertainty in Material Properties Considered (섬유 배열각의 이산성과 물성치의 불확실성을 고려한 복합재료 적층 평판의 최적 설계)

  • Kim, Tae-Uk;Sin, Hyo-Cheol
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.25 no.3
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    • pp.369-380
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    • 2001
  • Although extensive efforts have been devoted to the optimal design of composite laminated plates in recent years, some practical issues still need further research. Two of them are: the handling of the ply angle as either continuous or discrete; and that of the uncertainties in material properties, which were treated as continuous and ignored respectively in most researches in the past. In this paper, an algorithm for stacking sequence optimization which deals with discrete ply angles and that for thickness optimization which considers uncertainties in material properties are used for a two step optimization of composite laminated plates. In the stacking sequence optimization, the branch and bound method is modified to handle discrete variables; and in the thickness optimization, the convex modeling is used in calculating the failure criterion, given as constraint, to consider the uncertain material properties. Numerical results show that the optimal stacking sequence is found with fewer evaluations of objective function than expected with the size of feasible region taken into consideration; and the optimal thickness increases when the uncertainties of elastic moduli considered, which shows such uncertainties should not be ignored for safe and reliable designs.

Phasor Discrete Particle Swarm Optimization Algorithm to Configure Micro-grids

  • Bae, In-Su;Kim, Jin-O
    • Journal of Electrical Engineering and Technology
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    • v.7 no.1
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    • pp.9-16
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    • 2012
  • The present study presents the Phasor Discrete Particle Swarm Optimization (PDPSO) algorithm, an effective optimization technique, the multi-dimensional vectors of which consist of magnitudes and phase angles. PDPSO is employed in the configuration of micro-grids. Micro-grids are concepts of distribution system that directly unifies customers and distributed generations (DGs). Micro-grids could supply electric power to customers and conduct power transaction via a power market by operating economic dispatch of diverse cost functions through several DGs. If a large number of micro-grids exist in one distribution system, the algorithm needs to adjust the configuration of numerous micro-grids in order to supply electric power with minimum generation cost for all customers under the distribution system.

Genetic Algorithm Based Continuous-Discrete Optimization and Multi-objective Sequential Design Method for the Gear Drive Design (기어장치 설계를 위한 유전알고리듬 기반 연속-이산공간 최적화 및 다목적함수 순차적 설계 방법)

  • Lee, Joung-Sang;Chong, Tae-Hyong
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.16 no.5
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    • pp.205-210
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    • 2007
  • The integration method of binary and real encoding in genetic algorithm is proposed to deal with design variables of various types in gear drive design. The method is applied to optimum design of multi-stage gear drive. Integer and Discrete type design variables represent the number of teeth and module, and continuous type design variables represent face width, helix angle and addendum modification factor etc. The proposed genetic algorithm is applied for the gear ratio optimization and the volume optimization(minimization) of multi-stage geared motor which is used in field. In result, the proposed design optimization method shows an effectiveness in optimum design process and the new design has a better results compared with the existing design.

Discrete optimization of trusses using an artificial bee colony (ABC) algorithm and the fly-back mechanism

  • Fiouz, A.R.;Obeydi, M.;Forouzani, H.;Keshavarz, A.
    • Structural Engineering and Mechanics
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    • v.44 no.4
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    • pp.501-519
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    • 2012
  • Truss weight is one of the most important factors in the cost of construction that should be reduced. Different methods have been proposed to optimize the weight of trusses. The artificial bee colony algorithm has been proposed recently. This algorithm selects the lightest section from a list of available profiles that satisfy the existing provisions in the design codes and specifications. An important issue in optimization algorithms is how to impose constraints. In this paper, the artificial bee colony algorithm is used for the discrete optimization of trusses. The fly-back mechanism is chosen to impose constraints. Finally, with some basic examples that have been introduced in similar articles, the performance of this algorithm is tested using the fly-back mechanism. The results indicate that the rate of convergence and the accuracy are optimized in comparison with other methods.

The Effect of Sample and Particle Sizes in Discrete Particle Swarm Optimization for Simulation-based Optimization Problems (시뮬레이션 최적화 문제 해결을 위한 이산 입자 군집 최적화에서 샘플수와 개체수의 효과)

  • Yim, Dong-Soon
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.40 no.1
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    • pp.95-104
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    • 2017
  • This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.