PyXRD v0.6.7 : a free and open-source program to quantify disordered phyllosilicates using multi-specimen X-ray diffraction profile fitting

This paper presents a free and open-source program called PyXRD (short for Python X-ray diffraction) to improve the quantification of complex, poly-phasic mixed-layer phyllosilicate assemblages. The validity of the program was checked by comparing its output with Sybilla v2.2.2, which shares the same mathematical formalism. The novelty of this program is the ab initio incorporation of the multi-specimen method, making it possible to share phases and (a selection of) their parameters across multiple specimens. PyXRD thus allows for modelling multiple specimens side by side, and this approach speeds up the manual refinement process significantly. To check the hypothesis that this multi-specimen set-up – as it effectively reduces the number of parameters and increases the number of observations – can also improve automatic parameter refinements, we calculated X-ray diffraction patterns for four theoretical mineral assemblages. These patterns were then used as input for one refinement employing the multi-specimen set-up and one employing the single-pattern set-ups. For all of the assemblages, PyXRD was able to reproduce or approximate the input parameters with the multi-specimen approach. Diverging solutions only occurred in single-pattern set-ups, which do not contain enough information to discern all minerals present (e.g. patterns of heated samples). Assuming a correct qualitative interpretation was made and a single pattern exists in which all phases are sufficiently discernible, the obtained results indicate a good quantification can often be obtained with just that pattern. However, these results from theoretical experiments cannot automatically be extrapolated to all real-life experiments. In any case, PyXRD has proven to be useful when X-ray diffraction patterns are modelled for complex mineral assemblages containing mixed-layer phyllosilicates with a multi-specimen approach

Linearized biogeography-based optimization with re-initialization and local search

Biogeography-based optimization (BBO) is an evolutionary optimization algorithm that uses migration to share information among candidate solutions. One limitation of BBO is that it changes only one independent variable at a time in each candidate solution. In this paper, a linearized version of BBO, called LBBO, is proposed to reduce rotational variance. The proposed method is combined with periodic re-initialization and local search operators to obtain an algorithm for global optimization in a continuous search space. Experiments have been conducted on 45 benchmarks from the 2005 and 2011 Congress on Evolutionary Computation, and LBBO performance is compared with the results published in those conferences. The results show that LBBO provides competitive performance with state-of-the-art evolutionary algorithms. In particular, LBBO performs particularly well for certain types of multimodal problems, including high-dimensional real-world problems. Also, LBBO is insensitive to whether or not the solution lies on the search domain boundary, in a wide or narrow basin, and within or outside the initialization domain

A Novel Particle Swarm Optimization Algorithm

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2012Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2012Parçacık Sürü Optimizasyonu (Particle Swarm Optimization) (PSO) 1995’te Dr. Eberhart ve Dr. Kennedy tarafından geliştirilmiş popülasyon temelli sezgisel bir optimizasyon tekniğidir. Bu tekniğin bilinen dezavantajı, gerçek optimumu bulmadan önce erken yakınsama sergilenmesidir. Bu çalışmada, literatürde geleneksel hale gelen ve yeni parçacıkları popülasyona yeniden enjekte etmeyi öneren yöntemlere karşın, parçacıkları harici belleklerden alınan bilgilere göre yönlendiren yeni bir parçacık sürü optimizasyon algoritmasını sunmaktadır. Bunun için, parçacığın harici bellekteki parçacıklar arasından, parçacığa en yakın olan en iyi ve en kötü parçacığa uzaklığı hesaplanıp bir katsayi üretilmektedir. Sonra her parçacık için hız bileşenini hesaplarken bu katsayı belli bir olasılıkla mevcut hıza eklenmektedir. Ayrıca randomize bir üst ve alt sınır inertia için tanınmaktadır. Algoritmada inertia bileşeni üst sınırden başlayıp ve her parçacığı değerlendirdikten sonra küçük bir değerle non-linear bir şekilde azaltılmaktadır. Inertia bileşeni randomize alt sınıra ulaştığı zaman, bu bileşenin değeri randomize üst sınırın değeriyle sıfırlamaktadır. Çıkan PSO evrensel optima’yi orijinal PSO’den daha hızlı bulmaktadır. Bu algoritma en güncel PSO’ların arasında olan CLPSO algoritmasından daha hızlı olduğunu ve daha kaliteli çözümler ürettiğini ortaya çıkarılmıştır. Ayrıca literatürde en iyi ve güncel optimizasyon algoritmaların arasında olan CMA-ES algoritması de kıyaslamak amacıyla seçilmiştir. Deneylerin sonucunda, CMA-ES’in genelde PSO’den daha iyi olmasına rağmen, bazi durumlarda, sunulan yöntemin daha üstün bir performans sergilediği ortaya çıkarılmıştır. Evrensel optima’yı gösteren evrensel bir topolojinin eksik olduğu veya havzasının çeker hacmı küçük olan problemlerde, sunulan algoritma daha hızlı davrandığı gösterilmiştir. Deneyler, standart kriter olan fonksyonlar ve simülatör ortamındaki Aldebaran NAO robotun kick hareketi için uygulamaktadır.Particle Swarm Optimization (PSO) (Kennedy and Eberhart, 1995), which is a population-based global search method is known to suffer from premature convergence prior to discovering the true global minimizer. In this thesis, a novel memory-based method is proposed which aims to guide the particles through the information deduced from the external memory contents rather than to re-inject them into the population. This is done by calculating a coefficient, based on the distance of the current particle to the closest best and closest worst particles in the external memory at each iteration. Later, when updating the velocity component, this coefficient is added to the current velocity of the particle with a certain probability. Also randomized upper bound and lower bound values have been defined for the inertia component. The algorithm starts with the upper bound value of the inertia. At each particle evaluation the inertia is decreased non-linearly with a small value and when its value reaches the lower bound, the inertia value is reset to its upper bound. The resulting PSO finds the global optima much faster than the original PSO and it have been shown that it also performs better compared with a recent improvement of PSO, CLSPO namely. A state-of-the-art algorithm, CMA-ES (Covariance Matrix Adaptation Evolutionary Strategy), has also been chosen for comparison purposes. It has been shown by experiments that although the CMA-ES shows a better performance than that of our algorithm, in some cases where the overall topology pointing to the global optimum is missing and the attractor volume of global optimum is small, our algorithm performs better and finds the desired optimum value of the function in lesser evaluation counts. The tests have been consucted on standard benchmark functions as well as a simulation of the Aldebaran NAO robot for developing a kick action.Yüksek LisansM.Sc

Computational results for an automatically tuned CMA-ES with increasing population size on the CEC'05 benchmark set

Abstract In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC'05 benchmark set for continuous function optimization. In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC'05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search