Earticle

The design variables and material properties as well as the external loads concerned with structural engineering are used to be deterministic in optimization process. These values, however, have variability from expected performance. Therefore, deterministic optimum designs that are obtained without taking these uncertainty into account could lead to unreliable designs, which necessitates the Reliability-Based Design Optimization(RBDO). RBDO involves an evaluation of probabilistic constraints which constitutes another optimization procedure. So, an expensive computational cost is required. Therefore, how to decrease the computational cost has been an important challenge in the RBDO research field. Approximation models, response surface model and Kriging model, are employed to improve an efficiency of the RBDO.

The bat algorithm is a recently proposed meta-heuristic algorithm. Usually in solving the problem of optimization, the position of virtual bats is updated by flying speed, which decreases efficiency of the algorithm and accuracy of the solution. This paper has improved the location update strategy and individual selection strategy of bat algorithm, then puts forward an improved bat algorithm. The algorithm is used to solve three typical reliability-redundancy allocation problems, and the simulation experiment results show that the presented algorithm greatly enhances the computation efficiency, convergence speed and precision of the optimal solution in addressing the problem of reliability redundancy optimization. When compared with the best results reported in the previous literatures, the algorithm achieves a better or equally good optimal solution. It is demonstrated that the proposed algorithm is effective in solving reliability-redundancy allocation problems.

With the rapid development of computer network, the problem of reliability of computer network is becoming more and more attention of builders, users and the network designer. The reliability of computer network has become a key technical indicators to measure the comprehensive performance of computer network. Aiming at a single known computer network, applying to established the computer network link cost model and the computer network reliability model, and carries on the simulation using the genetic algorithm intelligent algorithm; comprehensive evaluation for a variety of known different computer network reliability index system, and we have given the evaluation results. The simulation results show that, the established computer network link cost model and the computer network reliability model in the thesis are suitable, model algorithm and programming language is effective, it has the practical application significance which the paper proposed the computer network reliability optimization design concrete plan.

A new algorithm combined Eagle Strategy with PSO is proposed. The new algorithm performs by two phases: First Eagle Strategy is used to do global search; Second PSO algorithm is used to do fast local search around a promising solution. The balance of global search and local search is considered simultaneously. It is not only help to jump out of local optimum but also accelerate local convergence. Experimental results on three benchmark problems illustrate that the presented approach is effective, efficient and accurate for solving reliability redundancy optimization problems.

This article is based on the mixture of tabu search algorithm and interior search algorithm(ISA) to address the reliability redundancy assignment problem. Iinterior search algorithm is immersed in a tabu search algorithm. TS is used to search solution space and ISA is applied to generate neighborhood solutions. The merit of two algorithms is considered at the same time. And a mixed TS-ISA method is proposed to deal with three benchmark reliability redundancy optimization problem. The experimental results show that a the method is effective and efficient for RRAP by comparing with other results in the previous literaturs.

In order to improve the motion reliability of space manipulator during its long time service on-orbit, multiple indexes which can reflect the motion performance and multiple factors (control variables of manipulator) which have influence on motion reliability are involved in establishing an optimization control model. Thus the problem of motion reliability improvement is expressed as an multiple-objective problem (MOP). In order to solve the MOP, dimension reduction strategy is proposed based on covariance matrix of motion reliability indexes. As a result, the MOP is turned into single objective problem. Then control variables are optimized based on null space of space manipulator, and the best weight matrix can be obtained when the objective is minimal. Simulations are carried out to verify the feasibility and effectiveness of the optimization control strategy. By decreasing the error introduction into the control variables, the motion reliability can be effectively improved.

A heuristic algorithm for reliability optimization of a distributed network system is developed so that the reliability of a large system can be determined efficiently. This heuristic bases on the determination of maximal reliability set of maximum node capacity, maximal link reliability and maximal node degree.

The redundancy allocation problem has usually considered only the component redundancy at the lowest-level for the enhancement of system reliability. A system can be functionally decomposed into system, module, and component levels. Modular redundancy can be more effective than component redundancy at the lowest-level because in modular systems, duplicating a module composed of several components can be easier, and requires less time and skill. We consider a multi-level redundancy allocation problem in which all cases of redundancy for system, module, and component levels are considered. A tabu search of memory-based mechanisms that balances intensification with diversification via the short-term and long-term memory is proposed for its solution. To the best of our knowledge, this is the first attempt to use a tabu search for this problem. Our tabu search algorithm is compared with the previous genetic algorithm for the problem on the new composed test problems as well as the benchmark problems from the literature. Computational results show that the proposed method outstandingly outperforms the genetic algorithm for almost all test problems.

The stability condition of rock slopes is greatly affected by the geometry and strength parameters of discontinuities in the rock masses. Rock slopes Involving movement of rock blocks on discontinuities are failed by one or combination of the three basic failure modes-plane, wedge, and toppling. In rock mechanics, practically all the parameters such as the joint set characteristics, the rock strength properties, and the loading conditions are always subject to a degree of uncertainty. Therefore, a reasonable assessment of the rock slope stability has to include the excavation of the multi-failure modes, the consideration of uncertainties of discontinuity characteristics, and the decision on stabilization measures with favorable cost conditions. This study was performed to provide a new numerical model of the deterministic analysis, reliability analysis, and reliability-based optimization for rock slope stability. The sensitivity analysis was carried out to verify proposed method and developed program; the parameters needed for sensitivity analysis are design variables, the variability of discontinuity properties (orientation and strength of discontinuities), the loading conditions, and rock slope geometry properties. The design variables to be optimized by the reliability-based optimization include the cutting angle, the support pressure, and the slope direction. The variability in orientations and friction angle of discontinuities, which can not be considered in the deterministic analysis, has a greatly influenced on the rock slope stability. The stability of rock slopes considering three basic failure modes is more influenced by the selection of slope direction than any other design variables. When either plane or wedge failure is dominant, the support system is more useful than the excavation as a stabilization method. However, the excavation method is more suitable when toppling failure is dominant. The case study shows that the developed reliability-based optimization model can reasonably assess the stability of rock slopes and reduce the construction cost.

A reliability-based optimization method for electromagnetic design is presented to take uncertainties of design parameters into account. The method can provide an optimal design satisfying a specified confidence level in the presence of uncertain parameters. To achieve the goal, the reliability index approach based on the firstorder reliability method is adopted to deal with probabilistic constraint functions and a double-loop optimization algorithm is implemented to obtain an optimum. The proposed method is applied to the TEAM Workshop Problem 22 and its accuracy and efficiency is verified with reference of Monte Carlo simulation results.

This paper presents a reliability-based topology optimization (RBTO) based on bidirectional evolutionary structural optimization (BESO). In design of a structure, uncertain conditions such as material property, operational load and dimensional variation should be considered. Deterministic topology optimization (DTO) is performed without considering the uncertainties related to the design variables. However, the RBTO can consider the uncertainty variables because it can deal with the probabilistic constraints. The reliability index approach (RIA) and the performance measure approach (PMA) are adopted to evaluate the probabilistic constraints in this study. In order to apply the BESO to the RBTO, sensitivity number for each element is defined as the change in the reliability index of the structure due to removal of each element. Smoothing scheme is also used to eliminate checkerboard patterns in topology optimization. The limit state indicates the margin of safety between the resistance (constraints) and the load of structures. The limit State function expresses to evaluate reliability index from finite element analysis. Numerical examples are presented to compare each optimal topology obtained from RBTO and DTO each other. It is verified that the RBTO based on BESO can be effectively performed from the results.

When Deterministic Design Optimization (DDO) methods are used, deterministic optimum designs are frequently pushed to the design constraint boundary, leaving little or no room for tolerances (or uncertainties) in design, manufacture, and operating processes. In the Reliability-Based Design Optimization (RBDO) model for robust system design, the mean values of uncertain system variables are usually used as design variables, and the cost is optimized subject to prescribed probabilistic constraints as defined by a nonlinear mathematical programming problem. Therefore, a RBDO solution that reduces the structural weight in uncritical regions does not only provide an improved design but also a higher level of confidence in the design. In this work, we seek to improve the quality of RBDO processes using efficient optimization techniques with object of improving the resulting objective function and satisfying the required constraints. Our recent RBDO developments show its efficiency and applicability in this context. So we present some recent structural engineering applications demonstrate the efficiency of these developed RBDO methods.

The objective of this work is to integrate reliability analysis into topology optimization problems. We introduce the reliability constraint in the topology optimization formulation, and the new model is called Reliability-Based Topology Optimization (RBTO). The application of the RBTO model gives a different topology relative to the classical topology optimization that should be deterministic. When comparing the structures resulting from the deterministic topology optimization and from the RBTO model, the RBTO model yields structures that are more reliable than the deterministic ones (for the same weight). Several applications show the importance of this integration.

Design variables for suspension systems cannot always be realized in the actual suspension systems due to tolerances in manufacturing and assembly processes. In order to deal with these tolerances, design variables associated with kinematic configuration and compliance characteristics of suspensions are treated as random variables. The reliability of a design target with respect to a design variable is defined as the probability that the design target is in the acceptable design range for all possible values of the design variable. To compute reliability, the limit state, which is the boundary between the acceptable and unacceptable design, is expressed mathematically by a limit state function with value greater than 0 for acceptable design, and less than 0 for unacceptable design. Through reliability analysis, the acceptable range of design variables that satisfy a reliability target is specified. Furthermore, through sensitivity analysis, a general procedure for optimization of the design target with respect to the design variables has been established.

This research proposes a reliability-based topology optimization (RBTO) using the finite element method. RBTO is a topology optimization based on probabilistic (or reliability) constraints. Young's modulus, thickness, and loading are considered as the uncertain variables and RBTO is applied to static and eigenvalue problems. The RBTO problems are formulated and a sensitivity analysis is performed. In order to compute probability constraints, two methods-RIA and PMA-are used. Several examples show the effectiveness of the proposed method by comparing the classical safety factor method.

This study introduces the Reliability-Based Design Optimization (RBDO) to enhance the kinematic and compliance (K & C) characteristics of automotive suspension system. In previous studies, the deterministic optimization has been performed to enhance the K & C characteristics. Unfortunately, uncertainties in the real world have not been considered in the deterministic optimization. In the design of suspension system, design variables with the uncertainties, such as the bushing stiffness, have a great influence on the variation of the suspension performances. There is a need to quantify these uncertainties and to apply the RBDO to obtain the design, satisfying the target reliability level. In this research, design variables including uncertainties are dealt as random variables and reliability of the suspension performances, which are related the K & C characteristics, are quantified and the RBDO is performed. The RBD-optimum is compared with the deterministic optimum to verify the enhancement in reliability. Thus, the reliability of the suspension performances is estimated and the RBD-optimum, satisfying the target reliability level, is determined.