Inverse problem of a buried metallic object

[[abstract]]In this paper we address an inverse scattering problem whose aim is to discuss the CPU time for recovering a perfectly conducting cylindrical object buried in a half-space. First, we use Fourier-series or cubic-spline methods to describe the shape and reformulate the inverse problem into an optimization one. Then we solved it by the improved steady state genetic algorithm (SSGA) and simple genetic algorithm (SGA) respectively and compare the cost time in finding out the global extreme solution of the objective function. It is found the searching ability of SSGA is much powerful than that of the SGA. Even when the initial guess is far away from the exact one, the cost time for converging to a global extreme solution using by SSGA is much less than that by SGA. Numerical results are given to show that the inverse problem by using SSGA is much better than SGA in time costing.[[notice]]需補地點及國別[[conferencetype]]國際[[conferencedate]]20051004~2005100

Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior

Improved genetic algorithm for phase-balancing in three-phase distribution networks: A master-slave optimization approach

This paper addresses the phase-balancing problem in three-phase power grids with the radial configuration from the perspective of master–slave optimization. The master stage corresponds to an improved version of the Chu and Beasley genetic algorithm, which is based on the multi-point mutation operator and the generation of solutions using a Gaussian normal distribution based on the exploration and exploitation schemes of the vortex search algorithm. The master stage is entrusted with determining the configuration of the phases by using an integer codification. In the slave stage, a power flow for imbalanced distribution grids based on the three-phase version of the successive approximation method was used to determine the costs of daily energy losses. The objective of the optimization model is to minimize the annual operative costs of the network by considering the daily active and reactive power curves. Numerical results from a modified version of the IEEE 37-node test feeder demonstrate that it is possible to reduce the annual operative costs of the network by approximately 20% by using optimal load balancing. In addition, numerical results demonstrated that the improved version of the CBGA is at least three times faster than the classical CBGA, this was obtained in the peak load case for a test feeder composed of 15 nodes; also, the improved version of the CBGA was nineteen times faster than the vortex search algorithm. Other comparisons with the sine–cosine algorithm and the black hole optimizer confirmed the efficiency of the proposed optimization method regarding running time and objective function value

Inverse Problem of an Embedded Metallic Cylinder

[[abstract]]In this paper we address an inverse scattering problem whose aim is to discuss the CPU time for recovering a perfectly conducting cylindrical object buried in a slab medium. First, we use Fourier-series or cubic- spline methods to describe the shape and reformulate the inverse problem into an optimization one. Then we solved it by the improved steady state genetic algorithm (SSGA) with different crossover rate and simple genetic algorithm (SGA) respectively and compare the cost time in finding out the global extreme solution of the objective function. It is found the searching ability of SSGA is much powerful than that of the SGA. Numerical results are given to show that the imaging problem by using SSGA is much better than SGA in time costing.[[conferencetype]]國際[[conferencedate]]20070625~20070630[[conferencelocation]]Kharkov, Ukrain

A Constraint Handling Strategy for Bit-Array Representation GA in Structural Topology Optimization

In this study, an improved bit-array representation method for structural topology optimization using the Genetic Algorithm (GA) is proposed. The issue of representation degeneracy is fully addressed and the importance of structural connectivity in a design is further emphasized. To evaluate the constrained objective function, Deb's constraint handling approach is further developed to ensure that feasible individuals are always better than infeasible ones in the population to improve the efficiency of the GA. A hierarchical violation penalty method is proposed to drive the GA search towards the topologies with higher structural performance, less unusable material and fewer separate objects in the design domain in a hierarchical manner. Numerical results of structural topology optimization problems of minimum weight and minimum compliance designs show the success of this novel bit-array representation method and suggest that the GA performance can be significantly improved by handling the design connectivity properly.Singapore-MIT Alliance (SMA

Optimizations of Patch Antenna Arrays Using Genetic Algorithms Supported by the Multilevel Fast Multipole Algorithm

We present optimizations of patch antenna arrays using genetic algorithms and highly accurate full-wave solutions of the corresponding radiation problems with the multilevel fast multipole algorithm (MLFMA). Arrays of finite extent are analyzed by using MLFMA, which accounts for all mutual couplings between array elements efficiently and accurately. Using the superposition principle, the number of solutions required for the optimization of an array is reduced to the number of array elements, without resorting to any periodicity and similarity assumptions. Based on numerical experiments, genetic optimizations are improved by considering alternative mutation, crossover, and elitism mechanisms. We show that the developed optimization environment based on genetic algorithms and MLFMA provides efficient and effective optimizations of antenna excitations, which cannot be obtained with array-factor approaches, even for relatively simple arrays with identical elements

Inverse Problem of Multiple Conductors Buried in a Half-Space

[[abstract]]Electromagnetic imaging of buried multiple conductors by using genetic algorithm has been presented. Two separate perfectly conducting cylinders of unknown shapes are immersed in one half-space and illuminated by transverse magnetic (TM) polarization plane wave from the other half-space. Based on the boundary condition and the measured scattered field, we have derived a set of nonlinear integral equations, and the imaging problem is reformulated into an optimization problem. For describing the shapes of conductors, the Fourier series is selected to expanding the shape functions. In inverse algorithms, the improved steady state genetic algorithm is employed to search for the global extreme solution of objective function. Numerical results have demonstrated that the powerful performance of the inverse algorithm. The reconstructed shapes are considerably accurate even when the initial guesses are far away from the exact ones and the buried depths of the conductors are large compared to wavelength.[[conferencetype]]國際[[conferencedate]]20070816~20070818[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Xi'an, Chin

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems

This study presents a new approach based on a hybrid algorithm consisting of Genetic Algorithm (GA), Pattern Search (PS) and Sequential Quadratic Programming (SQP) techniques to solve the well-known power system Economic dispatch problem (ED). GA is the main optimizer of the algorithm, whereas PS and SQP are used to fine tune the results of GA to increase confidence in the solution. For illustrative purposes, the algorithm has been applied to various test systems to assess its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA–PS–SQP algorithm is very efficient in solving power system economic dispatch problem