• Title, Summary, Keyword: discrete optimization

Search Result 455, Processing Time 0.044 seconds

Optimization of a Composite Laminated Structure by Network-Based Genetic Algorithm

  • Park, Jung-Sun;Song, Seok-Bong
    • Journal of Mechanical Science and Technology
    • /
    • v.16 no.8
    • /
    • pp.1033-1038
    • /
    • 2002
  • Genetic alsorithm (GA) , compared to the gradient-based optimization, has advantages of convergence to a global optimized solution. The genetic algorithm requires so many number of analyses that may cause high computational cost for genetic search. This paper proposes a personal computer network programming based on TCP/IP protocol and client-server model using socket, to improve processing speed of the genetic algorithm for optimization of composite laminated structures. By distributed processing for the generated population, improvement in processing speed has been obtained. Consequently, usage of network-based genetic algorithm with the faster network communication speed will be a very valuable tool for the discrete optimization of large scale and complex structures requiring high computational cost.

Optimal design of reinforced concrete beams: A review

  • Rahmanian, Ima;Lucet, Yves;Tesfamariam, Solomon
    • Computers and Concrete
    • /
    • v.13 no.4
    • /
    • pp.457-482
    • /
    • 2014
  • This paper summarizes available literature on the optimization of reinforced concrete (RC) beams. The objective of optimization (e.g. minimum cost or weight), the design variables and the constraints considered by different studies vary widely and therefore, different optimization methods have been employed to provide the optimal design of RC beams, whether as isolated structural components or as part of a structural frame. The review of literature suggests that nonlinear deterministic approaches can be efficiently employed to provide optimal design of RC beams, given the small number of variables. This paper also presents spreadsheet implementation of cost optimization of RC beams in the familiar MS Excel environment to illustrate the efficiency of the exhaustive enumeration method for such small discrete search spaces and to promote its use by engineers and researchers. Furthermore, a sensitivity analysis is performed on the contribution of various design parameters to the variability of the overall cost of RC beams.

Effect of Levy Flight on the discrete optimum design of steel skeletal structures using metaheuristics

  • Aydogdu, Ibrahim;Carbas, Serdar;Akin, Alper
    • Steel and Composite Structures
    • /
    • v.24 no.1
    • /
    • pp.93-112
    • /
    • 2017
  • Metaheuristic algorithms in general make use of uniform random numbers in their search for optimum designs. Levy Flight (LF) is a random walk consisting of a series of consecutive random steps. The use of LF instead of uniform random numbers improves the performance of metaheuristic algorithms. In this study, three discrete optimum design algorithms are developed for steel skeletal structures each of which is based on one of the recent metaheuristic algorithms. These are biogeography-based optimization (BBO), brain storm optimization (BSO), and artificial bee colony optimization (ABC) algorithms. The optimum design problem of steel skeletal structures is formulated considering LRFD-AISC code provisions and W-sections for frames members and pipe sections for truss members are selected from available section lists. The minimum weight of steel structures is taken as the objective function. The number of steel skeletal structures is designed by using the algorithms developed and effect of LF is investigated. It is noticed that use of LF results in up to 14% lighter optimum structures.

Observer-Teacher-Learner-Based Optimization: An enhanced meta-heuristic for structural sizing design

  • Shahrouzi, Mohsen;Aghabaglou, Mahdi;Rafiee, Fataneh
    • Structural Engineering and Mechanics
    • /
    • v.62 no.5
    • /
    • pp.537-550
    • /
    • 2017
  • Structural sizing is a rewarding task due to its non-convex constrained nature in the design space. In order to provide both global exploration and proper search refinement, a hybrid method is developed here based on outstanding features of Evolutionary Computing and Teaching-Learning-Based Optimization. The new method introduces an observer phase for memory exploitation in addition to vector-sum movements in the original teacher and learner phases. Proper integer coding is suited and applied for structural size optimization together with a fly-to-boundary technique and an elitism strategy. Performance of the proposed method is further evaluated treating a number of truss examples compared with teaching-learning-based optimization. The results show enhanced capability of the method in efficient and stable convergence toward the optimum and effective capturing of high quality solutions in discrete structural sizing problems.

Improved thermal exchange optimization algorithm for optimal design of skeletal structures

  • Kaveh, A.;Dadras, A.;Bakhshpoori, T.
    • Smart Structures and Systems
    • /
    • v.21 no.3
    • /
    • pp.263-278
    • /
    • 2018
  • Thermal Exchange Optimization (TEO) is a newly developed algorithm which mimics the thermal exchange between a solid object and its surrounding fluid. In this paper, an improved version of the TEO is developed to fix the shortcomings of the standard version. To demonstrate the viability of the new algorithm, the CEC 2016's single objective problems are considered along with the discrete size optimization of benchmark skeletal structures. Problem specific constraints are handled using a fly-back mechanism. The results show the validity of the improved TEO method compared to its standard version and a number of well-known algorithms.

Development of a Multi-objective function Method Based on Pareto Optimal Point (Pareto 최적점 기반 다목적함수 기법 개발에 관한 연구)

  • Na, Seung-Soo
    • Journal of the Society of Naval Architects of Korea
    • /
    • v.42 no.2
    • /
    • pp.175-182
    • /
    • 2005
  • It is necessary to develop an efficient optimization technique to optimize the engineering structures which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of engineering structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points by spreading point randomly entire the design spaces. In this paper, a Pareto optimal based multi-objective function method (PMOFM) is developed by considering the search direction based on Pareto optimal points, step size, convergence limit and random search generation . The PMOFM can also apply to the single objective function problems, and can consider the discrete design variables such as discrete plate thickness and discrete stiffener spaces. The design results are compared with existing Evolutionary Strategies (ES) method by performing the design of double bottom structures which have discrete plate thickness and discrete stiffener spaces.

Multi-Objective Fuzzy Optimization of Structures (구조물에 대한 다목적퍼지최적화)

  • Park, Choon-Wook;Pyeon, Hae-Wan;Kang, Moon-Myung
    • Journal of Korean Society of Steel Construction
    • /
    • v.12 no.5
    • /
    • pp.503-513
    • /
    • 2000
  • This study treats the criteria, considering the fuzziness occurred by optimization design. And we applied two weighting methods to show the relative importance of criteria. This study develops multi-objective optimization programs implementing plain stress analysis by FEM and discrete optimization design uniformaly. The developed program performs a sample design of 10-member steel truss. This study can carry over the multi-objective optimization based on total system fuzzy-genetic algorithms while performing the stress analysis and optimization design. Especially, when general optimization with unreliable constraints is cannot be solve this study can make optimization design closed to realistic with fuzzy theory.

  • PDF

Discrete Optimization for Vibration Design of Composite Plates by Using Lamination Parameters

  • Honda, Shinya;Narita, Yoshihiro;Sasaki, Katsuhiko
    • Advanced Composite Materials
    • /
    • v.18 no.4
    • /
    • pp.297-314
    • /
    • 2009
  • A design method is proposed to optimize the stacking sequence of laminated composite plates for desired vibration characteristics. The objective functions are the natural frequencies of the laminated plates, and three types of optimization problems are studied where the fundamental frequency and the difference of two adjacent frequencies are maximized, and the difference between the target and actual frequencies is minimized. The design variables are a set of discrete values of fiber orientation angles with prescribed increment in the layers of the plates. The four lamination parameters are used to describe the bending property of a symmetrically laminated plate, and are optimized by a gradient method in the first stage. A new technique is introduced in the second stage to convert from the optimum four lamination parameters into the stacking sequence that is composed of the optimum fiber orientation angles of all the layers. Plates are divided into sub-domains composed of the small number of layers and designed sequentially from outer domains. For each domain, the optimum angles are determined by minimizing the errors between the optimum lamination parameters obtained in the first step and the parameters for all possible discrete stacking sequence designs. It is shown in numerical examples that this design method can provide with accurate optimum solutions for the stacking sequence of vibrating composite plates with various boundary conditions.

A Study on Improvement in the Resistance Performance of Planing hulls by Hull Shape Optimization (고속활주선의 선형 최적화를 통한 저항성능 개선에 관한 연구)

  • Kim, Sunbum
    • Journal of the Korea Society for Simulation
    • /
    • v.27 no.2
    • /
    • pp.83-90
    • /
    • 2018
  • This paper describes the method of hull shape optimization to improve the resistance performance of planing hulls when a reference hull shape and its principal dimensions are given. First, the planing hull of precedent research is adopted as the reference hull and an optimization problem is formulated by defining hull shape parameters. The search space of this research is discretized for computing cost and DPSO(Discrete binary version of Particle Swarm Optimization) method is used to solve the optimization problem. As the result of optimization, the decrease of resistance is confirmed from the comparison between the reference hull's and the modified hull's planing performance from computational results.

Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

  • Gomathy, V.;Selvaperumal, S.
    • Journal of Power Electronics
    • /
    • v.16 no.3
    • /
    • pp.1097-1109
    • /
    • 2016
  • Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.