Genetic Pattern Search and Its Application to Brain Image Classification

Shale plays with pre-existing natural fractures can yield significant production when operating horizontal wells with multi-stage hydraulic fracturing (HWMHF). This work proposes a general, robust, and integrated framework for estimating optimal HWMHF design parameters in an unconventional naturally fractured oil reservoir. This work considers uncertainty in both the distribution of the natural fractures and uncertainty in three geo-mechanical parameters: the internal friction factor, the cohesion coefficient, and the tensile strength. Because a maximum of five design variables is considered, it is appropriate to apply derivative-free algorithms. This work considers versions of the genetic algorithm (GA), particle swarm optimization (PSO), and general pattern search (GPS) algorithms. The forward model consists of two linked software programs: a geo-mechanical simulator and an unconventional shale oil simulator. The two simulators run sequentially during the optimization process without human intervention. The in-house geo-mechanical simulator model provides sufficient computational efficiency so that it is feasible to solve the robust optimization problem. An embedded discrete fracture model (EDFM) is implemented to model large-scale fractures. Two cases strongly verified the feasibility of the framework for the optimization of HWMHF, and the average comprehensive NPV increases by 35% and 102.4%, respectively. By comparison, the pattern search algorithm is more suitable for HWMHF optimization. In this way, oil and gas scientists are contributing to the energy industry more accurately and resolutely.

Should Spinal MRI Be Routinely Performed in Patients With Clinically Isolated Optic Neuritis?

Novel computing paradigms for parameter estimation in Hammerstein controlled auto regressive auto regressive moving average systems

Features of several well-known global and local optimization methods were discussed and their robustness and efficiency were tested on several artificial test functions in Matlab environment. The tested local methods were the interior-point, the quasi-Newton method, Nelder–Mead simplex, the pattern search, the NEWUOA and the BOBYQA methods. The global methods were the genetic algorithm (GA), the simulated annealing (SA), the particle swarm optimization (PSO), and the covariance matrix adaptation evolutionary strategy (CMA-ES) methods (see subsections 2.2 and 2.3 for their details). Furthermore, a novel global optimization method, called FOCusing robusT Optimization with Uncertainty-based Sampling (FOCTOPUS), which proved to be very efficient in the optimization of constrained and highly correlated parameters of combustion kinetic models, was also tested. The test functions were selected in such a way that they had a variety of features: uni-modal and multi-modal, differentiable and non-differentiable, separable and non-separable, low dimensional and high dimensional. The following test functions were used: the 20D Alpine function, the 4D Ackley function, the Cross-in-tray 2D function, the Hartmann-6D function, the Holder table 2D function, the 5D Rastrigin function, the 5D Rosenbrock function, the 4D modified Rosenbrock function, the 20D Zakharov function and a typical 2D multi-modal function. The general conclusion here is that, the global methods performed well on the multi-modal and high-dimensional test functions while the local methods were superior in the case of low-dimensional and uni-modal test functions. For the highly multi-modal test functions, the GA was better than all the other methods. The FOCTOPUS method proved to be inferior to GA for most of the test functions, thus its application cannot be generally recommended.

A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization

In many applications, it is necessary to optimise the performance of hydrodynamic (HD) bearings. Many studies have proposed different strategies, but there remains a lack of conclusive research on the suitability of various optimisation methods. This study evaluates the most commonly used algorithms, including the genetic (GA), particle swarm (PSWM), pattern search (PSCH) and surrogate (SURG) algorithms. The effectiveness of each algorithm in finding the global minimum is analysed, with attention to the parameter settings of each algorithm. The algorithms are assessed on HD journal and thrust bearings, using analytical and numerical solutions for friction moment, bearing load-carrying capacity and outlet lubricant flow rate under multiple operating conditions. The results indicate that the PSCH algorithm was the most efficient in all cases, excelling in both finding the global minimum and speed. While the PSWM algorithm also reliably found the global minimum, it exhibited lower speed in the defined problems. In contrast, genetic algorithms and the surrogate algorithm demonstrated significantly lower efficiency in the tested problems. Although the PSCH algorithm proved to be the most efficient, the PSWM algorithm is recommended as the best default choice due to its ease of use and minimal sensitivity to parameter settings.

Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

Equivalent circuit model of induction motors was established and the formulas of power losses were given. Motor efficiency was optimized by Genetic Algorithm(GA) and Hybrid Genetic Algorithm(HGA). Power factor, breakdown torque ratio, locked-rotor torque ratio and locked-rotor current ratio were regarded as constraints and the optimal result is satisfying. Genetic Algorithm and Pattern Search Algorithm are integrated into a composite algorithm-Hybrid Genetic Algorithm. Motor efficiency rises further through Hybrid Genetic Algorithm.

Empirical comparison of search algorithms for discrete event simulation

This paper presents the results of an exploratory study aimed at investigating the impacts of optimisation of PV plant locations in South Africa in the context of the seasonal and diurnal cycles associated with the system load profiles and solar generation profiles. The distribution of a normalised per-unit generation capacity is optimised across a set of locations chosen to represent the diurnal cycle along a west-east axis and the seasonal cycle along a north-south axis, using local solar irradiance profiles for the candidate sites. Optimisations are conducted for a range of objective functions representing different scenarios defined in terms of seasonal considerations and time-of-use (TOU) periods using both genetic algorithm (GA) and pattern search methods in the MATLAB simulation environment. Comparative analysis of the optimisation results indicate that the latitude of PV plant locations plays a significant role in seasonal performance, while optimisation along a longitude dimension offers higher power generation during daily peak demand periods.

To differentiate onset of CNS involvement in primary hemophagocytic lymphohistiocytosis (HLH) from that of other CNS inflammatory diseases and to identify early symptoms linked to abnormal cognitive outcome. Forty-six children with primary HLH who had neurologic evaluation within 2 weeks and brain MRI within 6 months of diagnosis were included. Initial symptoms, CSF study, brain MRI, and neurologic outcome were assessed. Brain MRIs were compared with those of 44 children with acute disseminated encephalomyelitis (ADEM). At disease onset, 29 children (63%) had neurologic symptoms and 7 (15%) had microcephaly. Twenty-three (50%) children had abnormal CSF study, but only 15 (33%) had abnormal brain MRI. The latter showed that patients with HLH, unlike patients with ADEM, had symmetric periventricular lesions, without thalamic and brainstem involvement and with infrequent hyposignal intensity on T1. At the end of follow-up (3.6 ± 3.6 years), 17 of the 28 (61%) surviving patients had normal neurologic status, 5 (18%) had a severe neurologic outcome, and 6 (21%) had mild cognitive difficulties. Abnormal neurologic outcome was not influenced by age or type of genetic defect, but by the presence of neurologic symptoms, MRI lesions, or abnormal CSF study at onset. Early clinical and MRI symptoms may regress after treatment. Neurologic symptoms are frequent at the onset of primary HLH and are mostly associated with abnormal CSF findings, but with normal brain MRI. In cases of abnormal brain MRI, the observed lesions differ from those of ADEM.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

We present an open source toolbox in Scilab for multivariate polynomial optimization based on the Bernstein form. The developed toolbox finds the global minimum of unconstrained polynomial optimization problems. We first describe the method used by the toolbox, and then demonstrate its performance on several standard optimization examples. We also compare the quality of the results obtained using the developed toolbox with those of popular global optimization methods, such as genetic algorithms, simulated annealing, pattern search, and global search interior point methods. The tests show the superior performance of the presented toolbox.

A step-wise genetic algorithm (GA)-based approach is put forward for (a) guiding any interested country on how to optimally improve its environmental sustainability (ES), and (b) determining the characteristics of the (theoretically) maximally sustainable country. The Environmental Sustainability Index (ESI) 2005 is used to this end, with the GA genes, chromosomes, and fitness values corresponding to the ESI 2005 indicators, sets of indicator values, and scores, respectively. Following GA operation, any interested country can increase — and, ideally, maximise — its ES via the following procedure: a chromosome with similar gene values to the ESI indicator values of the interested country, but demonstrating higher fitness (ESI score), is appropriately selected from any GA generation such that the changes that are necessary in order for the country's ESI indicator values to match those of the selected chromosome to be not only implementable, but furthermore capable of producing the maximal improvement in terms of ES. By repeating this procedure on each updated version of the interested country, the country's indicator values and ESI score are progressively modified towards maximal ES. Alternative optimisation approaches, such as pattern search (PS) and simulated annealing (SA), are also capable of reaching maximal ES, yet — due to their single-state basis of operation — do not permit the concurrent investigation of alternative paths towards maximal ES, and — furthermore — do not guarantee the identification of the appropriate indicator(s) to be modified. An interesting finding that is common to GA, PS, and SA operation is that the fitness values of the best chromosomes/states invariably exceed not only the ESI score of the top-ranking ESI 2005 country (75.1 of Finland), but — furthermore — the “perfect” ESI 2005 score of 100 by around 10, thus putting into question the construction details of the ESI 2005.1

A data-driven optimization strategy based on a generalized pattern search (GPS) algorithm is proposed to automatically optimize piezoelectric energy harvesters (PEHs). As a direct search method, GPS can iteratively solve the derivative-free optimization problem. Taking the finite element method (FEM) as the solver and the GPS algorithm as the optimizer, the automatic interaction between the solver and optimizer ensures optimization with minimum human efforts, saving designers’ time and performing a more precise exploration in the parameter space to obtain better results. When employing it for the optimization of PEHs, the optimal length and thickness of PZT were 6.0 mm and 4.6 µm, respectively. Compared with reported high-output PEHs, this optimal structure showed an increase of 371% in output power, an improvement by 1000% in normalized power density, and a reduction of 254% in resonant frequency. Furthermore, Spearman’s rank correlation coefficient was calculated for evaluating the correlation among geometric parameters and output performance such as resonant frequency and output power, which provides a data-based perspective on the design and optimization of PEHs.