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Results for “differential evolution, evolutionary optimization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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23 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Experiments with a Parallel Multi-Objective Evolutionary Algorithm for Scheduling

Evolutionary multi-objective algorithms have great potential for scheduling in those situations where tradeoffs among competing objectives represent a key requirement. One challenge, however, is runtime performance, as a consequence of evolving not just a single schedule, but an entire population, while attempting to sample the Pareto frontier as accurately and uniformly as possible. The growing availability of multi-core processors in end user workstations, and even laptops, has raised the question of the extent to which such hardware can be used to speed up evolutionary algorithms. In this paper we report on early experiments in parallelizing a Generalized Differential Evolution (GDE) algorithm for scheduling long-range activities on NASA's Deep Space Network. Initial results show that significant speedups can be achieved, but that performance does not necessarily improve as more cores are utilized. We describe our preliminary results and some initial suggestions from parallelizing the GDE algorithm. Directions for future work are outlined.

scheduling↗
DOE OSTI2016

On the adaptivity and complexity embedded into differential evolution

This research deals with the comparison of the two modern approaches for evolutionary algorithms, which are the adaptivity and complex chaotic dynamics. This paper aims on the investigations on the chaos-driven Differential Evolution (DE) concept. This paper is aimed at the embedding of discrete dissipative chaotic systems in the form of chaotic pseudo random number generators for the DE and comparing the influence to the performance with the state of the art adaptive representative jDE. This research is focused mainly on the possible disadvantages and advantages of both compared approaches. Repeated simulations for Lozi map driving chaotic systems were performed on the simple benchmark functions set, which are more close to the real optimization problems. Obtained results are compared with the canonical not-chaotic and not adaptive DE. Results show that with used simple test functions, the performance of ChaosDE is better in the most cases than jDE and Canonical DE, furthermore due to the unique sequencing in CPRNG given by the hidden chaotic dynamics, thus better and faster selection of unique individuals from population, ChaosDE is faster.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Regulation of Viable and Optimal Cohorts

This study deals with the evolution of (scalar) attributes (resources or income in evolutionary demography or economics, position in traffic management, etc.) of a population of “mobiles” (economic agents, vehicles, etc.). The set of mobiles sharing the same attributes is regarded as an instantaneous cohort described by the number of its elements. The union of instantaneous cohorts during a mobile window between two attributes is a cohort. Given a measure defining the number of instantaneous cohorts, the accumulation of the mobile attributes on a evolving mobile window is the measure of the cohort on this temporal mobile window. Imposing accumulation constraints and departure conditions, this study is devoted to the regulation of the evolutions of the attributes which are1.viable in the sense that the accumulations constraints are satisfied at each instant;2.and, among them, optimal, in the sense that both the duration of the temporal mobile window is maximum and that the accumulation on this temporal mobile window is the largest viable one. This value is the “accumulation valuation” function. Viable and optimal evolutions under accumulation constraints are regulated by an “implicit Volterra integro-differential inclusion” built from the accumulation valuation function, solution to an Hamilton–Jacobi–Bellman partial differential equation under constraints which is constructed for this purpose.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Drive-pressure optimization in ramp-wave compression experiments through differential evolution

Ramp-wave dynamic-compression experiments are used to examine quasi-isentropic loading paths in materials. The gradual and continuous increase in pressure created by ramp waves make these types of experiments ideal for studying nonequilibrium material behavior, such as solidification kinetics. In ramp-wave compression experiments, the input drive pressure to the experimental setup may be exerted through one of a number of different mechanisms (e.g., magnetic fields, gas-gun-driven impactors, or high-energy lasers) and is generally required for simulating such experiments. Yet, regardless of the specific mechanism, this drive pressure cannot be measured directly (measurements are generally taken at a location near the back of the experimental setup through a transparent window), leading to an inverse problem where one must determine the drive pressure at the front of the experimental setup (i.e., the input) that corresponds to the particle velocity (the output) measured near the back of the experimental setup. Furthermore, we solve this inverse problem using a heuristic optimization algorithm, known as differential evolution, coupled with a multiphysics, hydrodynamics code that simulates the compression of the experimental setup. By running many rounds of forward simulations of the experimental setup, our optimization process iteratively searches for a drive pressure that is optimized to closely reproduce the experimentally measured particle velocity near the back of the experimental setup. While our optimization methodology requires a significant number of hydrodynamics simulations to be conducted, many of these can be performed in parallel, which greatly reduces the time cost of our methodology. One novel aspect of our method for determining the drive pressure is that it does not require physical modeling of the drive mechanism and can thus be broadly applied to many types of ramp-compression experiments, regardless of the drive mechanism.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

RELATIVE PROPER MOTIONS IN THE RHO OPHIUCHI CLUSTER

Near-infrared images optimized for astrometry have been obtained for four fields in the high-density L 1688 cloud core over a 12 year period. The targeted regions include deeply embedded young stellar objects (YSOs) and very low luminosity objects too faint and/or heavily veiled for spectroscopy. Relative proper motions in R.A. and decl. were computed for 111 sources and again for a subset of 65 YSOs, resulting in a mean proper motion of (0,0) for each field. Assuming each field has the same mean proper motion, YSOs in the four fields were combined to yield estimates of the velocity dispersions in R.A. and decl. that are consistent with 1.0 km s{sup −1}. These values appear to be independent of the evolutionary state of the YSOs. The observed velocity dispersions are consistent with the dispersion in radial velocity derived for optically visible YSOs at the periphery of the cloud core and are consistent with virial equilibrium. The higher velocity dispersion of the YSOs in the plane of the sky relative to that of dense cores may be a consequence of stellar encounters due to dense cores and filaments fragmenting to form small groups of stars or the global collapse of the L 1688 cloud core. An analysis of the differential magnitudes of objects over the 12 year baseline has not only confirmed the near-infrared variability for 29 YSOs established by prior studies, but has also identified 18 new variability candidates. Four of these have not been previously identified as YSOs and may be newly identified cluster members.

79 ASTRONOMY AND ASTROPHYSICS↗