Hybrid Genetic Programming with Local Search Operators for Dynamic Force Identification

In this paper, based on the Darwinian and Lamarckian evolution theories, three hybrid genetic programming (GP) algorithms integrated with different local search operators (LSOs) are implemented to improve the search efficiency of the standard GP. These three LSOs are the genetic algorithm, the linear bisection search, and the Hooke and Jeeves method. A simple encoding method is presented to encode the GP individuals into the expressions that can be recognized by the different LSOs. The implemented hybrid GP algorithms are applied to identify the excitation force acting on the structures from the measured structural response, which is an important type of inverse problem in structural dynamics. Illustrative examples of a frame structure and a multistory building structure demonstrate that, compared with the standard GP, the hybrid GP algorithms have higher search efficiency which can be used as alternate global search and optimization tools for other engineering problem solving.     

Pipe Index Vector: A Method to Improve Genetic-Algorithm-Based Pipe Optimization

This paper proposes a methodology for the optimal design of water distribution systems based on genetic algorithms. The objective of the optimization is to minimize the capital cost, subject to ensuring adequate pressures at all nodes during peak demands. The proposed method is novel in that it involves the use of a pipe index vector to control the genetic algorithm search. The pipe index vector is a measure of the relative importance of pipes in a network in terms of their impact on the hydraulic performance of the network. By using the pipe index vector it is possible to exclude regions of the search space where impractical and infeasible solutions exist. By reducing the search space it is possible to generate feasible solutions more quickly and hence process much healthier populations than would be the case in a standard genetic algorithm. This results in optimal solutions being found in a fewer number of generations resulting in a substantial saving in terms of computational time. The method has been tested on several networks, including networks used for benchmark testing least cost design algorithms, and has been shown to be efficient and robust.     

Integrated Genetic Algorithm for Optimization of Space Structures

Gradient‐based mathematical‐optimization algorithms usually seek a solution in the neighborhood of the starting point. If more than one local optimum exists, the solution will depend on the choice of the starting point, and the global optimum cannot be found. This paper presents the optimization of space structures by integrating a genetic algorithm with the penalty‐function method. Genetic algorithms are inspired by the basic mechanism of natural evolution, and are efficient for global‐searches. The technique employs the Darwinian survival‐of‐the‐fittest theory to yield the best or better characters among the old population, and performs a random information exchange to create superior offspring. Different types of crossover operations are used in this paper, and their relative merit is investigated. The integrated genetic algorithm has been implemented in C language and is applied to optimization of three space truss structures. In each case, an optimum solution was obtained after a limited number of iterations.     

Genetic Programming-Based Approach for Structural Optimization

The main purpose of this paper is to introduce genetic programming into civil engineering problem solving. This paper describes a genetic programming-based approach for simultaneous sizing, geometry, and topology optimization of structures. An encoding strategy is presented to map between the real structures and the genetic programming parse trees. Numerical results for two examples reveal that the proposed approach is capable of producing the topology and shape of the desired trusses and the sizing of all the structural components. Hence, this approach can potentially be a powerful search and optimization technique in civil engineering problem solving.     

Tank Simulation for the Optimization of Water Distribution Networks

In this paper an original approach to the simulation of floating-on-the-system tanks as decision variables for water distribution system design optimization is presented, aiming to bridge the gap between traditional engineering practice and mathematical considerations needed for genetic algorithms (GAs). The paper includes a systematic and detailed critical overview of various mathematical approaches in literature, as well as a novel, more “engineering oriented” approach to the simulation of tanks as decision variables for water distribution system design optimization, describing in detail assumptions and impacts to the evaluation of potential solutions. Tank simulation is based on two decision variables: capacity and minimum normal operational level, omitting risers. Shape and ratio between emergency/total capacities are taken into consideration as design parameters. Assessment of tank performance is carried out by four criteria for the normal daily operational cycle, differentiating between operational and filling capacity, as well as two further criteria for emergency flows. The original design and operational mathematical assumptions are implemented in a fuzzy multiobjective GA model, which is applied to the well-known example from literature “Anytown” water distribution network to benchmark the results.     

Competent Genetic-Evolutionary Optimization of Water Distribution Systems

A genetic algorithm has been applied to the optimal design and rehabilitation of a water distribution system. Many of the previous applications have been limited to small water distribution systems, where the computer time used for solving the problem has been relatively small. In order to apply genetic and evolutionary optimization technique to a large-scale water distribution system, this paper employs one of competent genetic-evolutionary algorithms—a messy genetic algorithm to enhance the efficiency of an optimization procedure. A maximum flexibility is ensured by the formulation of a string and solution representation scheme, a fitness definition, and the integration of a well-developed hydraulic network solver that facilitate the application of a genetic algorithm to the optimization of a water distribution system. Two benchmark problems of water pipeline design and a real water distribution system are presented to demonstrate the application of the improved technique. The results obtained show that the number of the design trials required by the messy genetic algorithm is consistently fewer than the other genetic algorithms.     

Efficient Parallel Implementation of Hybrid Optimization Approaches for Solving Groundwater Inverse Problems

Inverse problems that are constrained by large-scale partial differential equation (PDE) systems demand very large computational resources. Solutions to these problems generally require the solution of a large number of complex PDE systems. Three-dimensional groundwater inverse problems fall under this category. In this paper, we describe the implementation of a parallel simulation-optimization framework for solving PDE-based inverse problems and demonstrate it for the solution of groundwater contaminant source release history reconstruction problems that are of practical importance. The optimization component employs several optimization algorithms, including genetic algorithms (GAs) and several local search (LS) approaches that can be used in a hybrid mode. This hybrid GA-LS optimizer is used to drive a parallel finite-element (FEM) groundwater forward transport simulator. Parallelism is exploited within the transport simulator (fine grained parallelism) as well as the optimizer (coarse grained parallelism) through the exclusive use of the Message Passing Interface (MPI) communication library. Algorithmic and parallel performance results are presented for an IBM SP3 supercomputer. Simulation and performance results presented in this paper illustrate that an effective combination of efficient optimization algorithms and parallel computing can enable solution to three-dimensional groundwater inverse problems of a size and complexity not attempted before.     

Concurrent Genetic Algorithms for Optimization of Large Structures

In a recent article, the writers presented an augmented Lagrangian genetic algorithm for optimization of structures. The optimization of large structures such as high‐rise building structures and space stations with several hundred members by the hybrid genetic algorithm requires the creation of thousands of strings in the population and the corresponding large number of structural analyses. In this paper, the writers extend their previous work by presenting two concurrent augmented Lagrangian genetic algorithms for optimization of large structures utilizing the multiprocessing capabilities of high‐performance computers such as the Cray Y‐MP 8/864 supercomputer. Efficiency of the algorithms has been investigated by applying them to four space structures including two high‐rise building structures. It is observed that the performance of both algorithms improves with the size of the structure, making them particularly suitable for optimization of large structures. A maximum parallel processing speed of 7.7 is achieved for a 35‐story tower (with 1,262 elements and 936 degrees of freedom), using eight processors.     

Robust Parallel Algorithms for Solution of Riccati Equation

Robust and efficient parallel-vector algorithms are presented for the solution of the Riccati equations encountered in optimal control problems on shared-memory multiprocessor machines. The algorithms have been implemented on a Cray YMP 8∕8128 and applied to three large problems resulting from a continuous bridge structure, a 21-story space truss structure, and a 12-story space moment-resisting building structure. Efficiency of the algorithms is presented in terms of millions of floating point operations per second (MFLOPS) and the speedup. The MFLOPS for the largest example resulting from the 12-story space frame structure is a high 206.0. The speedup due to parallel processing only (for the same example), using seven processors, is 6.33. When vectorization is combined with parallel processing a very significant speedup of 54.4 is obtained using seven processors. The algorithms developed in this research find applications in the complex integrated control∕structural optimization problem. Further, the writers are currently using them to develop large adaptive∕smart structures.     

Engineering Analysis with Uncertainties and Complexities, Using Reasoning Approaches

Conventional computation methods generally limit practicing engineers from using complex formulations or considering uncertainties in general. A method is needed that can be implemented regardless of the uncertainty or linearity of the design parameters and their constraints. Methods such as qualitative reasoning provide an effective and sound technique for solving complex and uncertain scenarios. Uncertainties in engineering designs can be formulated as variables in the application domain and processed by numerical constraint reasoning. This paper describes the theories and algorithms behind a software platform built upon numerical constraint reasoning for engineering applications. The capability of representing design parameters and outcomes in a 2D solution space provides a practical way for engineers to leverage their existing knowledge and experience. The software expresses the results of the analysis in variable ranges and diagrams showing a 2D design space. Qualitative reasoning can assist in the difficult process of making appropriate engineering assumptions and judgments when carrying out complicated analysis procedures. In addition, interval constraint analysis can be used to derive controlling parameters and design space, therefore giving engineers a good overall understanding of a problem when practical experience is not available.     

Intestinal lymph absorption of butter, corn oil, cod liver oil, menhaden oil, and eicosapentaenoic and docosahexaenoic acid ethyl esters in rats

The purpose of this work was to develop a computer program that assists optimization of controlled-release devices, both visually and mathematically, using response surface methodology (RSM). A Windows-based computer program, Optima, which interactively implemented a number of subroutines for the optimization procedure, was developed. Optima is an integrated, user-friendly, and graphically oriented program for pharmaceutical dosage form optimization. Central composite design is implemented in the program. First- and second-order models containing up to five variables can be fitted to the data. The user can also choose between linear and exponential individual desirability functions, and use them to construct an overall desirability function that combines all the response variables in a single response. The program can predict the optimum levels of experimental variables, with respect to individual responses and/or the overall desirability. Optima has been successfully used in the development of sustained-release AZT-loaded microspheres. During the optimization process, three experimental variables were investigated and four responses were measured. The experimental design was a central composite design that was generated by the program. The response values were used by the program to calculate the individual desirability functions, which were then combined into an overall desirability function. The individual responses as well as the overall desirability function were optimized by fitting to a second-order polynomial equation. The response surfaces were generated and optimum levels of the experimental variables were predicted. The observed responses of the optimized formulation were very close to those predicted by Optima. The program proved to be a very useful, integrated tool for optimization of the controlled-release microspheres.     

Object-Oriented Optimization Approach Using Genetic Algorithms for Lattice Towers

A new approach is presented for the optimization of steel lattice towers by combining genetic algorithms and an object-oriented approach. The purpose of this approach is to eliminate the difficulties in the handling of large size problems such as lattice towers. Improved search and rapid convergence are obtained by considering the lattice tower as a set of small objects and combining these objects into a system. This is possible with serial cantilever structures such as lattice towers. A tower consists of panel objects, which can be classified as separate objects, as they possess an independent property as well as inherent properties. This can considerably reduce the design space of the problem and enhance the result. An optimization approach for the steel lattice tower problem using objects and genetic algorithms is presented here. The paper also describes the algorithm with practical design considerations used for this approach. To demonstrate the approach, a typical tower configuration with practical constraints has been considered for discrete optimization with the new approach and compared with the results of a normal approach in which the full tower is considered.     

图书馆文化在图书馆发展中的若干思考

文章就图书馆文化在图书馆建设发展中的定位、特征、作用等进行考量,力图说明图书馆文化不仅服务于图书馆建设发展,更是图书馆建设发展的精神动力。     

Genetic-Algorithm-Based Strategies for Dynamic Identification of Nonlinear Systems with Noise-Corrupted Response

The main objective of this paper is to investigate efficiency and correctness of different real-coded genetic algorithms and identification criteria in nonlinear system identification within the framework of non-classical identification techniques. Two conventional genetic algorithms have been used, standard genetic algorithm and microgenetic algorithm. Moreover, an advanced multispecies genetic algorithm has been proposed: it combines an adaptive rebirth operator, a migration strategy, and a search space reduction technique. Initially, a critical analysis has been conducted on these soft computing strategies to provide some guidelines for similar engineering and physical applications. Therefore, the hysteretic Bouc-Wen model has been numerically investigated to achieve three main results. First, the computational effectiveness and accuracy of the proposed strategy are checked to show that the proposed optimizer outperforms the aforementioned conventional genetic algorithms. Secondarily, a comparative study is performed to show that an improved performance can be obtained by using the Hilbert transform-based acceleration envelope as objective function in the optimization problem (instead of the pure acceleration response). Finally, system identification is conducted by making use of the proposed optimizer to verify its substantial noise-insensitive property also in the presence of high noise-to-signal ratio.     

Parallel Computing Techniques for Sensitivity Analysis in Optimum Structural Design

Among different activities of the optimum structural design using the gradient-based optimization approaches, design sensitivity analysis is the most time-consuming computational process. By introducing parallel computing techniques for sensitivity computation, significant speedup has been obtained in optimum structural design. Computation of design sensitivities is characteristically uncoupled, thus opening the door to parallelization. In this paper, two types of approaches viz. single-level and multilevel parallelisms are pursued for design sensitivities. The design sensitivities are computed using analytical and finite-difference methods. Numerical studies show that the performance of the parallel algorithms for design sensitivities on message passing systems is very good. Good speedups have been achieved in parallel multilevel sensitivity calculation. The parallel algorithms for design sensitivity analysis have been implemented on message passing parallel systems within the software platform of Parallel Computer Adaptive Language.     

Optimization Research: Enhancing the Robustness of Large-Scale Multiobjective Optimization in Construction

Many construction planning problems require optimizing multiple and conflicting project objectives such as minimizing construction time and cost while maximizing safety, quality, and sustainability. To enable the optimization of these construction problems, a number of research studies focused on developing multiobjective optimization algorithms (MOAs). The robustness of these algorithms needs further research to ensure an efficient and effective optimization of large-scale real-life construction problems. This paper presents a review of current research efforts in the field of construction multiobjective optimization and two case studies that illustrate methods for enhancing the robustness of MOAs. The first case study utilizes a multiobjective genetic algorithm (MOGA) and an analytical optimization algorithm to optimize the planning of postdisaster temporary housing projects. The second case study utilizes a MOGA and parallel computing to optimize the planning of construction resource utilization in large-scale infrastructure projects. The paper also presents practical recommendations based on the main findings of the analyzed case studies to enhance the robustness of multiobjective optimization in construction engineering and management.     

Towards the physical map of the Trypanosoma cruzi nuclear genome: construction of YAC and BAC libraries of the reference clone T. cruzi CL-Brener

Strategies to construct the physical map of the Trypanosoma cruzi nuclear genome have to capitalize on three main advantages of the parasite genome, namely (a) its small size, (b) the fact that all chromosomes can be defined, and many of them can be isolated by pulse field gel electrophoresis, and (c) the fact that simple Southern blots of electrophoretic karyotypes can be used to map sequence tagged sites and expressed sequence tags to chromosomal bands. A major drawback to cope with is the complexity of T. cruzi genetics, that hinders the construction of a comprehensive genetic map. As a first step towards physical mapping, we report the construction and partial characterization of a T. cruzi CL-Brener genomic library in yeast artificial chromosomes (YACs) that consists of 2,770 individual YACs with a mean insert size of 365 kb encompassing around 10 genomic equivalents. Two libraries in bacterial artificial chromosomes (BACs) have been constructed, BACI and BACII. Both libraries represent about three genome equivalents. A third BAC library (BAC III) is being constructed. YACs and BACs are invaluable tools for physical mapping. More generally, they have to be considered as a common resource for research in Chagas disease.     

Parallel Genetic Algorithms for Optimizing Resource Utilization in Large-Scale Construction Projects

This paper presents the development of a parallel multiobjective genetic algorithm framework to enable an efficient and effective optimization of resource utilization in large-scale construction projects. The framework incorporates a multiobjective optimization module, a global parallel genetic algorithm module, a coarse-grained parallel genetic algorithm module, and a performance evaluation module. The framework is implemented on a cluster of 50 parallel processors and its performance was evaluated using 183 experiments that tested various combinations of construction project sizes, numbers of parallel processors and genetic algorithm setups. The results of these experiments illustrate the new and unique capabilities of the developed parallel genetic algorithm framework in: (1) Enabling an efficient and effective optimization of large-scale construction projects; (2) achieving significant computational time savings by distributing the genetic algorithm computations over a cluster of parallel processors; and (3) requiring a limited and feasible number of parallel processors/computers that can be readily available in construction engineering and management offices.     

Gastric and duodenal anti-ulcer activity of sulpiride, a dopamine D2 receptor antagonist, in rats

The buildings in which we house libraries are like other special purpose structures; the needs they fill are significantly influenced by technology. A prime function of the library building is to house collections (of people, material, and systems) as well as collections of collections (networks). Electronic formats for library material offer new approaches to information service delivery. An example, the information access station, typifies how traditional functions can be reconfigured with respect to space. Flexible design can help ensure that tomorrow's libraries meet the users' needs, but we need to question all our assumptions about building design including those driven by our understanding of the browsing process.