Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009

pp. 1689-1696This paper presents results of the BBOB-2009 benchmark- ing of 31 search algorithms on 24 noiseless functions in a black-box optimization scenario in continuous domain. The runtime of the algorithms, measured in number of function evaluations, is investigated and a connection between a sin- gle convergence graph and the runtime distribution is uncov- ered. Performance is investigated for different dimensions up to 40-D, for different target precision values, and in dif- ferent subgroups of functions. Searching in larger dimension and multi-modal functions appears to be more difficult. The choice of the best algorithm also depends remarkably on the available budget of function evaluations

Optimization of the passive recovery of uranium from seawater

textThe aim of this thesis is to optimize the design and deployment conditions utilized by a technology for passively collecting uranium from seawater that is currently under development by Oak Ridge and Pacific Northwest National Labs along with University partners. This system involves the production, deployment, and recycle of an amidoxime ligand grafted onto a high density polyethylene based adsorbent. While many adsorbent performance characteristics and cost inputs impact the final uranium production cost, the system and design parameters explored here include: degree of ligand grafting, number of adsorbent uses prior to ultimate disposal, length of immersion in the sea, and ocean temperature. Given the complicated empirically-driven nature of the cost calculation, the cost calculation tool is treated as a black box model, thus the minimization requires a derivative free optimization method. A literature review is conducted to explore applicable algorithms and the Nelder-Mead Simplex Method is ultimately selected. A base case is created using historical values to serve as an initial condition for optimization. From this case, the uranium production cost is minimized, resulting in an 11% decrease. From there, sensitivity cases are considered. An alternative elution process for recovering uranium from the adsorbent is studied. If this innovation can be realized, significant cost savings are shown to be attained if this process fulfills its promise of mitigating adsorbent degradation. Next, the effects of marine bacterial growth on cost are explored. It is determined that optimizing the deployment conditions and improving the uranium binding kinetics can mitigate this increase. Sensitivity analyses are conducted in order to provide insight as to how the optimal deployment conditions are determined. The results presented in this thesis can inform the direction of future research. Furthermore, as the technology continues to evolve, the methodology developed for this optimization will remain relevant and the optimization too can continue to be used to guide design and R&D decisions.Mechanical Engineerin

Linear Spectral Unmixing Algorithms for Abundance Fraction Estimation in Spectroscopy

Fluorescence spectroscopy is commonly used in modern biological and chemical studies, especially for cellular and molecular analysis. Since the measured fluorescence spectrum is the sum of the spectrum of each fluorophore in a sample, a reliable separation of fluorescent labels is the key to the successful analysis of the sample. A technique known as linear spectral unmixing is often used to linearly decompose the measured fluorescence spectrum into a set of constituent fluorescence spectra with abundance fractions. Various algorithms have been developed for linear spectral unmixing. In this work, we implement the existing linear unmixing algorithms and compare their results to discuss their strengths and drawbacks. Furthermore, we apply optimization methods to the linear unmixing problem and evaluate their performance to demonstrate their capabilities of solving the linear unmixing problem. Finally, we denoise noisy fluorescence emission spectra and examine how noise may affect the performance of the algorithms

関数最適化問題に対する適応型差分進化法の研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学准教授 福永 アレックス, 東京大学教授 池上 高志, 東京大学教授 植田 一博, 東京大学教授 山口 泰, 東京大学教授 伊庭 斉志University of Tokyo(東京大学

Author manuscript, published in "ACM-GECCO Genetic and Evolutionary Computation Conference (2009)" Benchmarking the Nelder-Mead Downhill Simplex Algorithm With Many Local Restarts

We benchmark the Nelder-Mead downhill simplex method on the noisefree BBOB-2009 testbed. A multistart strategy is applied on two levels. On a local level, at least ten restarts are conducted with a small number of iterations and reshaped simplex. On the global level independent restarts are launched until 10 5 D function evaluations are exceeded, for dimension D ≥ 20 ten times less. For low search space dimensions the algorithm shows very good results on many functions. It solves 24, 18, 11 and 7 of 24 functions in 2, 5