Parallel Micro-genetic Algorithm Research Articles

Genetic Algorithm Embedded with a Search Space Dimension Reduction Scheme for Efficient Peptide Structure Predictions.

The computational determination of peptide conformations is a challenging task of finding minima in a high dimensional space. By combining the sampling efficiency of the genetic algorithm (GA) and the dimensionality reduction resulted from the backbone dihedral angle correlations, named as the path matrix (PM) method, a new searching algorithm, parallel microgenetic algorithm (PMGA), is proposed. Meanwhile, PMGA employs the density functional theory based energy as the fitness function and performs local geometry optimizations to enhance the reliability of its GA encoding strategy. Tests on peptides with up to eight amino-acid residues show PMGA is quite efficient for providing high-quality conformational coverages. The computational cost of the PMGA search increases slowly with the number of amino-acid residues in a peptide, with no sign of deterioration on the searching results for the increased length of the peptide. The PMGA method should therefore be useful for determining the conformations of oligopeptide, studying the protein-ligand interactions, and designing the peptide-based drugs.

FAST SYNTHESIS OF LARGE PLANAR ARRAYS USING ACTIVE ELEMENT PATTERN METHOD AND FINE-GRAINED PARALLEL MICRO-GENETIC ALGORITHM

A radiation pattern synthesis technique for large planar arrays with active element pattern (AEP) method and flne-grained parallel micro-genetic algorithm (FGPMGA) is presented. Based on the AEP method, the mutual coupling between array elements can be taken into account. Analysis problems of large rectangular and triangular grid planar arrays are divided into small linear array problems. And for a multiple concentric circular ring array, we only need to obtain one-sixth of all AEPs. So computational cost is greatly reduced. Large planar arrays with low side lobe level (SLL) can be achieved via optimizing the excitation amplitudes and phases. In order to reduce the global optimization time, the FGPMGA is used. This technique is applied to design 256-element rectangular grid, 200- element triangular grid and 4-circle 60-element concentric circular ring E-shaped patch antenna arrays. The radiation patterns calculated by the AEP method show good agreements with those by using CST Microwave Studio.

THE FINE-GRAINED PARALLEL MICRO-GENETIC ALGORITHM AND ITS APPLICATION TO BROADBAND CONICAL CORRUGATED-HORN ANTENNA

The flne-grained parallel micro-genetic algorithm (FGP- MGA) is developed to solve antenna design problems. The synthesis of uniformly exited unequally spaced array is presented. Comparison with the micro-genetic algorithm (MGA) has been carried out. It is seen that the FGPMGA signiflcantly outperforms MGA, in terms of both the convergence rate and exploration ability. The FGPMGA can also reduce the optimization time. Then the FGPMGA and the body of revolution flnite-difierence time-domain (BOR-FDTD) are combined to achieve an automated design process for conical corrugated-horn antenna. Numerical simulation results show that the horn antenna has good impedance matching (the VSWR is less than 1.5), stable beamwidth and gain, as well as good rotation symmetry patterns over the whole band 8»13GHz.

DESIGN OF AN ULTRA-WIDEBAND POWER DIVIDER VIA THE COARSE-GRAINED PARALLEL MICRO-GENETIC ALGORITHM

An ultra-wideband (UWB) power divider is designed in this paper. The UWB performance of this power divider is obtained by using a tapered microstrip line that consists of exponential and elliptic sections. The coarse grained parallel micro-genetic algorithm (PMGA) and CST Microwave Studio are combined to achieve an automated parallel design process. The method is applied to optimize the UWB power divider. The optimized power divider is fabricated and measured. The measured results show relatively low insertion loss, good return loss, and high isolation between the output ports across the whole UWB (3.1{10.6GHz).

Systematic design process for slanted grating couplers

We have developed a systematic design process for recently proposed slanted grating couplers (SGCs) that operates in the strong coupling regime. Based on rigorous analysis of SGC properties, this design process utilizes the k-vector diagram and a rigorous grating leaky-mode solver to intentionally enforce the phase-match and Bragg conditions. We demonstrate that the resultant SGC designs have performance similar to those obtained by parallel micro-genetic algorithm (muGA) optimization with the two-dimensional finite-difference time-domain (2D FDTD) method. Only two 2D FDTD simulations are necessary in the later stages of our systematic design process. Therefore the time saving is tremendous compared to a muGA 2D FDTD design tool, which can require thousands of individual 2D FDTD simulation runs. We illustrate the utility of our new systematic design process with an embedded slanted grating coupler example.

Reply to “Comments on `Design of a Frequency Selective Surface (FSS) With Very Low Cross-Polarization Discrimination via the Parallel Micro-Genetic Algorithm (PMGA)'”

The present authors give a reply to the paper by Munk and Pryor (IEEE Trans. Antennas Propagat., vol.52, no.4, p.1135-6, 2004) which commented on the original paper by Chakravarty and Mittra (IEEE Trans. Antennas Propagat., vol.51, p.1664-8, 2003 July).

Parallel microgenetic algorithm design for photonic crystal and waveguide structures.

We have developed a powerful parallel genetic algorithm design tool for photonic crystal and waveguide structures. The tool employs a small-population-size genetic algorithm (microgenetic algorithm) for global optimization and a two-dimensional finite-difference time-domain method to rigorously design and optimize the performance of photonic devices. We discuss the implementation and performance of this design tool. We demonstrate its application to two photonic devices, a defect taper coupler to connect conventional waveguides and photonic crystal waveguides, and a sharp 90 degrees waveguide bend for low index contrast waveguides.

Design of a frequency selective surface (FSS) with very low cross-polarization discrimination via the parallel micro-genetic algorithm (PMGA)

We discuss the design of a frequency selective surface (FSS) with low cross-polarization discrimination. In the past, FSS screens have been employed to improve the cross-polarization performance. The objective of this paper is to synthesize an FSS subreflector with low cross-polarization via the application of a parallel binary coded micro-genetic algorithm (PMGA). The PMGA optimizes the metallization pattern and the periodicity of the FSS screen in both the x- and y-directions to maintain a low cross-polarization level for a wide band of frequencies and a range of incident angles.

Parallel micro genetic algorithm for constrained economic dispatch

This paper proposes a parallel micro genetic algorithm (PMGA) for solving ramp rate constrained economic dispatch (ED) problems for generating units with nonmonotonically and monotonically increasing incremental cost (IC) functions. The developed PMGA algorithm is implemented on the thirty-two-processor Beowulf cluster with ethernet switches network on the systems with the number of generating units ranging from 10 to 80 over the entire dispatch periods. The PMGA algorithm carefully schedules its processors, computational loads, and synchronization overhead for the best performance. The speedup upper bounds and the synchronization overheads on the Beowulf cluster are shown on different system sizes and different migration frequencies. The proposed PMGA is shown to be viable to the online implementation of the constrained ED due to substantial generator fuel cost savings and high speedup upper bounds.

Parallel Micro Genetic Algorithm for Constrained Economic Dispatch

This paper proposes a parallel micro genetic algorithm (PMGA) for solving ramp rate constrained economic dispatch (ED) problems for generating units with nonmonotonically and monotonically increasing incremental cost (IC) functions. The developed PMGA algorithm is implemented on the 32-processor Beowulf cluster with Ethernet switches network on the systems with the number of generating units ranging from 10 to 80 over the entire dispatch periods. The PMGA algorithm carefully schedules its processors, computational loads, and synchronization overhead for the best performance. The speedup upper bounds and the synchronization overheads on the Beowulf cluster are shown on different system sizes and different migration frequencies. The proposed PMGA is shown to be viable to the online implementation of the constrained ED due to substantial generator fuel cost savings and high speedup upper bounds.