Efficient spreading strategy for global solution search using a trust-region optimization method: an application to CRS stacking problem

Some processes in seismic imaging can be formulated as a coherence-based problem, such as common reflection surface (CRS) stacking.The approach consists of obtaining the CRS attributes that provide the best fitting CRS surface in the multi-coverage data. The problem can be described as an optimization problem and solved by an optimization algorithm. Generally, quick convergent optimization algorithms are local solvers. To obtain the global solution, an efficient strategy is proposed to be used combined with a trust-region local optimization method. This strategy can be divided into two features: sequential parameters search and spreading solution.The idea is to first find solutions on a coarse output grid by sequential parameter search. This feature is based on constructing splines to estimate the maxima of the objective function in one dimension. These estimated maxima are the initial approximations to the local solver.The optimization algorithm obtains the parameters by sequentially solving one, two, and three-dimensional problems. Once the solutions are found on the coarse grid, useful information is propagated in the neighborhood to obtain the solutions on all output grid. Although the idea of spreading solution seems easy, its implementation is complex. It is essential to consider the properties of the problem as well as the properties of the optimization algorithm. Through some numerical experiments, the results using this strategy are shown. The use of sequential parameter search and spreading solution provides an improvement not only in the parameters but also in computational time.

A memetic algorithm enables global all-atom protein-protein docking with sidechain flexibility

ABSTRACTProtein-protein docking plays a central role in the characterization and discovery of protein interactions in the cell. Complex formation is encoded by specific interactions at the atomic scale, but the computational cost of modeling proteins at this level often requires the use of simplified energy models, coarse-grained protein descriptions and rigid-body approximations. In this study we present EvoDOCK, which is an evolutionary-based docking algorithm that enables the identification of optimal docking orientations using an atomistic energy function and sidechain flexibility, employing a global search without prior information of the binding site. EvoDOCK is a memetic algorithm that combines the strength of a differential evolution algorithm for efficient exploration of the global search space with the benefits of a local optimization method, built on the Monte Carlo-based RosettaDOCK program, to optimize detailed atomic interactions. This approach resulted in substantial improvements in both sampling efficiency and computation speed compared to calculations using the local optimization method RosettaDOCK alone, with up to 35 times of reduction in computational cost. For all the ten systems investigated in this study, a highly accurate docking prediction could be identified as the lowest energy model with high efficiency. While protein-protein docking with EvoDOCK is still computationally expensive compared to many methods based on Fast Fourier Transforms (FFT), the results demonstrate the tractability of global docking proteins using an atomistic energy function while exploring sidechain flexibility. Comparison with FFT global docking demonstrated the benefits of using an all-atom energy function to identify native-like predictions. The sampling strategy in EvoDOCK can readily be tailored to include backbone flexibility in the search, which is often necessary to tackle more challenging docking challenges.

Advanced Completion Optimization ACO: A Comprehensive Workflow for Flow Control Devices

There is a growing interest in downhole flow control devices (FCD) as they can be used to counter the effects of reservoir heterogeneity and improve hydrocarbon recovery. The variety of FCD types, sizes, and specifications makes it challenging to select the right device, providing an optimal investment return. Reservoir engineers play an essential role in identifying and evaluating the possible options based on numerical simulation models. This paper utilizes a transient numerical optimizer, designed for FCD with active controls, as a generic tool for lower completion optimization, including passive and autonomous FCD. The workflow consists of five steps. The first step is to determine a practical number of completion zones in a well, given the reservoir heterogeneity and completion string considerations. The second step is to explore the potential gains of downhole control without accounting for device-specific limitations. Here, we integrate a local optimization method to run several simulations with reactive and proactive strategies. In the third step, we contrast the local optimization simulations results to determine the suitable family of FCD: passive, autonomous or active. In step four, we ensure the selected FCD family is correctly modelled in reservoir simulation, particularly for autonomous devices, based on a recently published method to calculate equipment specific flow coefficients. Finally, optimization runs are performed using the selected FCD family and specific coefficients. The workflow is demonstrated on a synthetic carbonate sector model with a ~2,000 m horizontal well. It can be shown that analyses of the local optimization results provide quick guidelines to screen and select suitable lower completion equipment for the well and reservoir. Furthermore, the local optimization results can be used to design the selected lower completion string. The suggested workflow is the first of its kind in that it caters to all FCD families. The workflow uses an efficient local optimization method in a next-generation reservoir simulator. The efficiency gains from using the optimizer allows the decision-making time required for FCD evaluation to be an order of magnitude less than the logging and completion operation timeframe. Hence, the workflow enables efficient real-time design, planning and optimization of FCD.

Robust Fixed Low-Order Controller for Uncertain Decoupled MIMO Systems

The design of a robust fixed low-order controller for uncertain decoupled multi-input multi-output (MIMO) systems is proposed in this paper. The simplified decoupling is used as a decoupling system technique. In this work, the real system behavior is described by a linear model with parametric uncertainties. The main objective of the control law is to satisfy, in presence of model uncertainties, some step response performances such as the settling time and the overshoot. The controller parameters are obtained by resolving a min–max nonconvex optimization problem. The resolution of this kind of problems using standard methods can generate a local solution. Thus, we propose, in this paper, the use of the generalized geometric programming (GGP) which is a global optimization method. Simulation results and a comparison study between the presented approach, a proportional integral (PI) controller, and a local optimization method are given in order to shed light the efficiency of the proposed controller.

Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO), inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

Assessment of optimized Markov models in protein fold classification

Protein fold classification is a challenging task strongly associated with the determination of proteins' structure. In this work, we tested an optimization strategy on a Markov chain and a recently introduced Hidden Markov Model (HMM) with reduced state-space topology. The proteins with unknown structure were scored against both these models. Then the derived scores were optimized following a local optimization method. The Protein Data Bank (PDB) and the annotation of the Structural Classification of Proteins (SCOP) database were used for the evaluation of the proposed methodology. The results demonstrated that the fold classification accuracy of the optimized HMM was substantially higher compared to that of the Markov chain or the reduced state-space HMM approaches. The proposed methodology achieved an accuracy of 41.4% on fold classification, while Sequence Alignment and Modeling (SAM), which was used for comparison, reached an accuracy of 38%.

Design of Multifunctional Truss-Like Periodic Materials Using a Global-Local Optimization Method

Porous materials have gained wide use in high level engineering structures due to their high stiffness/weight ratio, good energy absorption properties, etc. Frequently, thermal behavior is also an issue of concern and optimized multifunctional thermo-mechanical responses are sought for. This paper presents the application of a hybrid two-stage method for achieving an optimized layout of periodic truss-like structures in order to obtain a good compromise between thermal and mechanical elastic properties. The first stage employs a derivative free optimization method, which explores the design space, not getting trapped by local minima. The second stage uses a derivative based optimization algorithm to perform a refinement of the solution obtained in the first stage.