Endmembers abundance estimators based on spectral shape similarity using genetic algorithm and generalized pattern search algorithm

A parallel micromanipulator (PmM) which can be applied into the precision machining is optimized in the paper. This paper adopts a methodology to determine a set of optimal design parameters of PmM whose workspace is as close as possible of being equal to a prescribed cuboid dexterous workspace (PCDW). The kinematic problem is analyzed in brief to determine the design parameters and their relation. Then, an optimal design procedure which adopts the generalized pattern search algorithm in the genetic algorithm and direct search toolbox of Matlab is proposed to solve these problems. As an applying example, the results of four cases PCDW to PmM are presented. And the design result is compared with a concept of the distance between the best state of the PmM and the requirement of the operation task. The method and result of this paper are very useful for the design of micromanipulator for the machining field which requires the high precision and accuracy.

Download Full-text

A Design of Parameters with Supercritical Carbon Dioxide Brayton Cycle for CiADS

Science and Technology of Nuclear Installations ◽

10.1155/2018/3245604 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9

Author(s):

Yichuan He ◽

Aihua Dong ◽

Min Xie ◽

Yang Liu

Keyword(s):

Carbon Dioxide ◽

Genetic Algorithm ◽

Supercritical Carbon Dioxide ◽

Thermal Efficiency ◽

Search Algorithm ◽

Pattern Search ◽

Outlet Temperature ◽

Brayton Cycle ◽

Pattern Search Algorithm ◽

Supercritical Carbon

Recompression supercritical carbon dioxide (SCO2) Brayton Cycle for the Chinese Initiative Accelerator Driven System (CiADS) is taken into account, and flexible thermodynamic modeling method is presented. The influences of the key parameters on thermodynamic properties of SCO2 Brayton Cycle are discussed and the comparative analyses on genetic algorithm and pattern search algorithm are conducted. It is shown that the cycle parameters such as turbine inlet temperature, pressure ratio, outlet temperature at the hot end of condenser, and terminal temperature difference of regenerator 1 and regenerator 2 have significant effects on the cycle thermal efficiency. The calculation results indicate that pattern search algorithm has better optimization performance and quicker calculating speed than genetic algorithm. The result of optimization of the parameters for CiADS with supercritical carbon dioxide Brayton Cycle is 35.97%. Compared with other nuclear power plants of SCO2 Brayton Cycle, CiADS with SCO2 Brayton Cycle does not have the best thermal efficiency, but the thermal efficiency can be improved with the reactor outlet temperature increases.

Download Full-text

A Generalized Pattern Search Algorithm Methodology for solving an Under-Determined System of Equality Constraints to achieve Power System Observability using Synchrophasors

Journal of Physics Conference Series ◽

10.1088/1742-6596/2090/1/012125 ◽

2021 ◽

Vol 2090 (1) ◽

pp. 012125

Author(s):

Nikolaos P. Theodorakatos ◽

Miltiadis Lytras ◽

Rohit Babu

Keyword(s):

Power System ◽

Cost Function ◽

Search Algorithm ◽

Pattern Search ◽

Equality Constraints ◽

Pattern Search Algorithm ◽

Generalized Pattern Search ◽

The Impact ◽

Linear Cost ◽

Number Of Iterations

Abstract The impact of the generalized pattern search algorithm (GPSA) on power system complete observability utilizing synchrophasors is proposed in this work. This algorithmic technique is an inherent extension of phasor measurement unit (PMU) minimization in a derivative-free framework by evaluating a linear objective function under a set of equality constraints that is smaller than the decision variables in number. A comprehensive study about the utility of such a system of equality constraints under a quadratic objective has been given in our previous paper. The one issue studied in this paper is the impact of a linear cost function to detect optimality in a shorter number of iterations, whereas the cost is minimized. The GPSA evaluates a linear cost function through the iterations needed to satisfy feasibility and optimality criteria. The other issue is how to improve the performance of convergence towards optimality using a gradient-free mathematical algorithm. The GPSA detects an optimal solution in a fewer number of iterations than those spent by a recursive quadratic programming (RQP) algorithm. Numerical studies on standard benchmark power networks show significant improvement in the maximum observability over the existing measurement redundancy generated by the RQP optimization scheme already published in our former paper.

Download Full-text