In this paper we propose to incorporate gender in genetic algorithms. Existing genetic algorithms are gender-neutral. Every genetic algorithm which is gender-neutral can be easily constructed as a gender-specific gene...
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In this paper we propose to incorporate gender in genetic algorithms. Existing genetic algorithms are gender-neutral. Every genetic algorithm which is gender-neutral can be easily constructed as a gender-specific genetic algorithm. We compared the performance of the gender-neutral and its gender-specific counterpart on four optimization problems and the gender-specific algorithm exhibited superior performance. A statistical analysis allows this conclusion to be stated with 99.5% confidence.
Efficient diagnosis of faults in digital circuits requires high quality diagnostic test sets that are generated by effective algorithm. In this work, novel optimization algorithms for diagnostic test generation are pr...
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ISBN:
(纸本)0780393953
Efficient diagnosis of faults in digital circuits requires high quality diagnostic test sets that are generated by effective algorithm. In this work, novel optimization algorithms for diagnostic test generation are proposed that require significantly less time than previous methods. The diagnostic test generation is performed using particle swarm optimization (PSO) or improved PSO, and fault simulator. Experimental results illustrate the effectiveness of the approach. STPG based on improved PSO is generally superior to both GA-based STPG and PSO-based STPG in terms of achieved fault coverage and required CPU time.
In this paper the most suitable algorithms for unconstrained optimization now available applied to an identification inverse problem in elasticity using the boundary element method (BEM) are compared, Advantage is tak...
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In this paper the most suitable algorithms for unconstrained optimization now available applied to an identification inverse problem in elasticity using the boundary element method (BEM) are compared, Advantage is taken of the analytical derivative of the whole integral C, equation of the BEM with respect to the variation of the geometry, direct differentiation, which can be used to obtain the gradient of the cost function to be optimized. (C) 2002 Elsevier Science Ltd. All rights reserved.
In this paper, various architectures of three-dimensional compact microwave balanced to unbalanced (balun) transformers for Bluetooth/WiFi antenna applications are successfully designed and optimized using the design ...
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In this paper, various architectures of three-dimensional compact microwave balanced to unbalanced (balun) transformers for Bluetooth/WiFi antenna applications are successfully designed and optimized using the design of experiments (DOE) approach. Two different multilayer topologies, one microstrip and one stripline, are investigated on low temperature co-fired ceramic (LTCC) substrate. The design goals for both baluns are perfectly balanced outputs from 2 to 3 GHz and a resonant frequency of exactly 2.4 GHz. It is demonstrated, using only eight simulations, that perfectly balanced outputs are not possible under the given conditions in the case of the microstrip balun. Nevertheless, the stripline balun can be optimized due to its almost symmetrical structure, and both simulations and measurement results verify the conclusions. The DOE method is very simple to implement and gives a clear understanding of the system behavior at the beginning of the design process, reducing the amount of work required for achieving the design goals by orders of magnitude compared to the widely used trial-and-error approach. The matching and unique measurement issues regarding the calibration, placement of probes and the de-embedding of the microstrip to coplanar waveguide transitions are discussed in detail for the optimized stripline balun. This technique can be easily applied to the fast and efficient optimization of complicated radiation structures, such as reconfigurable or multilayer mutliband antenna arrays.
In this paper, we propose an optimization framework for maximizing asset value, both with and without uncertainty. We first present the methodology to treat a general control optimization in the presence of uncertaint...
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In this paper, we propose an optimization framework for maximizing asset value, both with and without uncertainty. We first present the methodology to treat a general control optimization in the presence of uncertainty, followed by a brief section on the optimization algorithms used. We then describe the field model example used to illustrate the application of the methodology. Through a systematic analysis of various deterministic and stochastic cases, we address the various objectives sought. Using net present value (NPV) as a measure, we also explore the valuation of advanced completions along with the returns gained from expanding surface gas-handling facilities. The method also generates an efficient frontier that can be used for risk and decision analysis. The results clearly demonstrate the value of such a framework for value maximization in planning both near- and long-term time horizons as well as providing the necessary foundation for maximizing asset value.
Order-value optimization (OVO) is a generalization of the minimax problem motivated by decision-making problems under uncertainty and by robust estimation. New optimality conditions for this nonsmooth optimization pro...
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Order-value optimization (OVO) is a generalization of the minimax problem motivated by decision-making problems under uncertainty and by robust estimation. New optimality conditions for this nonsmooth optimization problem are derived. An equivalent mathematical programming problem with equilibrium constraints is deduced. The relation between OVO and this nonlinear-programming reformulation is studied. Particular attention is given to the relation between local minimizers and stationary points of both problems.
This paper presents the use of numerical simulations coupled with optimization techniques in oil reservoir modeling and production optimization. We describe three main components of an autonomic oil production managem...
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This paper presents the use of numerical simulations coupled with optimization techniques in oil reservoir modeling and production optimization. We describe three main components of an autonomic oil production management framework. The framework implements a dynamic, data-driven approach and enables Grid-based large scale optimization formulations in reservoir modeling. (C) 2004 Elsevier B.V. All rights reserved.
We propose an improved algorithm for unconstrained global optimization in the framework of the Moore-Skelboe algorithm of interval analysis (H. Ratschek and J. Rokne, New computer methods for global optimization, Wile...
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We propose an improved algorithm for unconstrained global optimization in the framework of the Moore-Skelboe algorithm of interval analysis (H. Ratschek and J. Rokne, New computer methods for global optimization, Wiley, New York, 1988). The proposed algorithm is an improvement over the one recently proposed in P.S.V. Nataraj and K. Kotecha, (J. Global optimization, 24 (2002) 417). A novel and powerful feature of the proposed algorithm is that it uses a variety of inclusion function forms for the objective function-the simple natural inclusion, the Taylor model (M. Berz and G. Hoffstatter, Reliable Computing, 4 (1998) 83), and the combined Taylor-Bernstein form (P.S.V. Nataraj and K. Kotecha, Reliable Computing, in press). Several improvements are also proposed for the combined Taylor-Bernstein form. The performance of the proposed algorithm is numerically tested and compared with those of existing algorithms on 11 benchmark examples. The results of the tests show the proposed algorithm to be overall considerably superior to the rest, in terms of the various performance metrics chosen for comparison.
Many real-world applications involve complex optimization problem with various competing specifications and constraints that are often difficult, if not impossible, to be solved without the aid of powerful and efficie...
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ISBN:
(纸本)0780393635
Many real-world applications involve complex optimization problem with various competing specifications and constraints that are often difficult, if not impossible, to be solved without the aid of powerful and efficient optimization algorithms. Although evolutionary algorithms have proven to be successful with respect to the optimization goals of proximity and diversity, their capability is bottlenecked by the evolutionary operators' abilities to deal with the complicated search spaces. Furthermore, it is well known that the algorithm's performances in different problems are sensitive to the parameter setting of the operators. In an effort to adapt the evolutionary search ability along the different regions of the search space, this paper proposes a dynamic variation operator whose parameter value will be deterministically adapted during the algorithm run so as to maintain a balance between the extensive exploration in the early phase and local fine-tuning in the end phase. Comparative studies with some representative variation operators are performed on different benchmark problems to illustrate the effectiveness and efficiency of the proposed operator.
In the case of optimizing the circuit configurations such as multi-way power dividers, the planar circuit approach is useful because of its merit of short calculation time. However, as the number of design variable in...
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ISBN:
(纸本)078039433X
In the case of optimizing the circuit configurations such as multi-way power dividers, the planar circuit approach is useful because of its merit of short calculation time. However, as the number of design variable increases, the CPU time required in the optimization becomes large. This paper describes a parallel computing technique of Powell's optimization algorithm using a PC-Cluster, and applies to an integration design of microstrip multi-way power dividers. As a result, it is shown that the parallel processing technique can speed up the circuit optimization with facility.
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