Bat Algorithm: Literature Review and Applications

Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.Comment: 10 page

DEVELOPMENT OF EFFICIENTLY COUPLED FLUID FLOW AND GEOMECHANICS MODEL FOR HIGHLY FRACTURED RESERVOIRS

Number of unconventional developments have increased greatly in the recent years to meet the global demand on hydrocarbon usages. Completion work can be very challenging due to complex characteristic of unconventional reservoir, which directly affects production performance. A rapid decline in parent well production has recently been observed in many unconventional developments, which subsequently increases the number of infill wells. Hydraulic fractures created from infill wells tend to propagate towards the parent well as a result of reservoir depletion. The interference between parent and infill well fractures due to a tight spacing is the main cause of poor production performance in both parent and infill wells. Stress change can be observed as the reservoir depletes due to the poroelastic effect. This leads to complex fracture geometry created during infill well completion, which is difficult to predict and usually causes negative impact on well production. Therefore, it is important to be able to predict depletion-induced stress change in the reservoirs with complex fracture geometries. The prediction of fracture interference is sometimes not accurate compared to the field observation as most studies mainly focus on stress evolution in planar fracture geometries since it is difficult to model complex fracture geometries. Unstructured grids have been implemented to handle such problems, but it usually comes with high computational cost and less computational efficiency, which is not a good option when simulating a field-scaled reservoir. This has become the main motivation of this work, which is to develop a coupled geomechanics and fluid flow model to characterize stress evolution due to reservoir depletion in highly fractured reservoirs with high computational efficiency. In this dissertation, I have developed a coupled geomechanics and fluid flow using a well-known sequentially coupled method called fixed stress-split to capture stress change in both magnitude and orientation during reservoir depletion. The coupling method was selected to ensure stability of the simulation while maintaining low computational cost. Embedded Discrete Fracture Model (EDFM) was coupled with the model to gain capability in simulating complex fracture geometries using structured gridding system. This significantly improves computational efficiency as well as opens the possibility of exploring cases with complex fracture network. The simulator was developed based on an open-source code called Open source Field Operation And Maniputation (OpenFOAM), allowing the simulation to be conducted in full 3D without significantly impacting computational cost. The developed model was used to predict refracturing performance in a highly fractured reservoir as well as infill well completion in a multi-payzone reservoir. In addition, the model was coupled with complex fracture propagation model to study how heterogeneous stress state affects fracture geometry created during infill well treatment, which can greatly help predict fracture interaction and maintain production performance. Two-phase flow was also implemented to the model for some field case studies such as water injection. The results observed in this study suggest that fracture geometry is a main factor that affects stress change in magnitude and orientation. The presence of natural fractures and fracture spacing plays an important role in refracturing performance in highly fractured reservoirs. Critical time can be used to determine when the refracturing should be performed to ensure the successful results and obtain optimum refracturing locations. For the infill well completion in a reservoir with multiple pay zones, it is suggested that both parent wells should be placed in different layers to mitigate stress change in the infill zone. Fracture penetration effect should also be considered as it accelerates stress reorientation in the infill zone. Severe asymmetrical fracture geometries with the longer side being closer to the depleted zone can be observed in the infill well with short spacing when coupling fracture propagation model with the reservoir-geomechanics model. These results are crucial and can be a guideline for field operation in reservoirs with complex fracture network

Flower pollination algorithm: a novel approach for multiobjective optimization

Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed

Flower pollination algorithm: a novel approach for multiobjective optimization

Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed

Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU

The utilization of optimization algorithms within engineering problems has had a major rise in recent years, which has led to the proliferation of a large number of new algorithms to solve optimization problems. In addition, the emergence of new parallelization techniques applicable to these algorithms to improve their convergence time has made it a subject of study by many authors. Recently, two optimization algorithms have been developed: Teaching-Learning Based Optimization and Jaya. One of the main advantages of both algorithms over other optimization methods is that the former do not need to adjust specific parameters for the particular problem to which they are applied. In this paper, the parallel implementations of Teaching-Learning Based Optimization and Jaya are compared. The parallelization of both algorithms is performed using manycore GPU techniques. Different scenarios will be created involving functions frequently applied to the evaluation of optimization algorithms. Results will make it possible to compare both parallel algorithms with regard to the number of iterations and the time needed to perform them so as to obtain a predefined error level. The GPU resources occupation in each case will also be analyzed.This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Grant TIN2017-89266-R, in part by FEDER funds (MINECO/FEDER/UE), and in part by the Spanish Ministry of Science, Innovation, and Universities co-financed by FEDER funds under Grant RTI2018-098156-B-C54

Lévy-Flight Krill Herd Algorithm

To improve the performance of the krill herd (KH) algorithm, in this paper, a Lévy-flight krill herd (LKH) algorithm is proposed for solving optimization tasks within limited computing time. The improvement includes the addition of a new local Lévy-flight (LLF) operator during the process when updating krill in order to improve its efficiency and reliability coping with global numerical optimization problems. The LLF operator encourages the exploitation and makes the krill individuals search the space carefully at the end of the search. The elitism scheme is also applied to keep the best krill during the process when updating the krill. Fourteen standard benchmark functions are used to verify the effects of these improvements and it is illustrated that, in most cases, the performance of this novel metaheuristic LKH method is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic KH

A Pareto-Based Sensitivity Analysis and Multiobjective Calibration Approach for Integrating Streamflow and Evaporation Data

Evaporation is gaining increasing attention as a calibration and evaluation variable in hydrologic studies that seek to improve the physical realism of hydrologic models and go beyond the long-established streamflow-only calibration. However, this trend is not yet reflected in sensitivity analyses aimed at determining the relevant parameters to calibrate, where streamflow has traditionally played a leading role. On the basis of a Pareto optimization approach, we propose a framework to integrate the temporal dynamics of streamflow and evaporation into the sensitivity analysis and calibration stages of the hydrologic modeling exercise, here referred to as “Pareto-based sensitivity analysis” and “multiobjective calibration.” The framework is successfully applied to a case study using the Variable Infiltration Capacity (VIC) model in three catchments located in Spain as representative of the different hydroclimatic conditions within the Iberian Peninsula. Several VIC vegetation parameters were identified as important to the performance estimates for evaporation during sensitivity analysis, and therefore were suitable candidates to improve the model representation of evaporative fluxes. Sensitivities for the streamflow performance, in turn, were mostly driven by the soil and routing parameters, with little contribution from the vegetation parameters. The multiobjective calibration experiments were carried out for the most parsimonious parameterization after a comparative analysis of the performance gains and losses for streamflow and evaporation, and yielded optimal adjustments for both hydrologic variables simultaneously. Results from this study will help to develop a better understanding of the trade-offs resulting from the joint integration of streamflow and evaporation data into modeling frameworks.ALHAMBRA cluster (http://alhambra. ugr.es) of the University of GranadaProject P20_00035, funded by the FEDER/ Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades, the project CGL2017-89836-RThe Spanish Ministry of Economy and CompetitivenessEuropean Community Funds (FEDER)The project PID2021- 126401OB-I00MCIN/ AEI/10.13039/501100011033/FEDER Una manera de hacer Europa and the project LifeWatch-2019-10-UGR-01 funded by FEDER/Ministerio de Ciencia e InnovaciónThe Ministry of Education, Culture and Sport of Spain through an FPU Grant (reference FPU17/02098)Aid for Research Stays in the Hydrology and Quantitative Water Management Group of Wageningen University (reference EST19/00169)Universidad de Granada/CBU

Gradient-based quantum optimal control on superconducting qubit systems

Quantum technologies are expected to help solve many of today's global challenges, revolutionizing several fields such as computing, sensing and secure communications. In this regard, the need for precise manipulation of the dynamics of a quantum system and its optimization has given rise to the field of quantum control theory. In the search for optimal controls, accurate derivatives are a possible method to traverse and ultimately converge in quantum optimization landscapes. In this work we study an efficient algorithm for computing analytically-exact derivatives by formulating the control problem in the basis that diagonalizes the control Hamiltonian and applying a specific Trotterized time propagation scheme. The method is numerically verified for a system of superconducting transmon qubits in the few- and many body regime using matrix product states. The comparison between the results obtained using an exact dynamics via Krylov subspace methods shows how the approximate dynamics ultimately sets a trade-off between computational complexity and quality of the final solutions.Quantum technologies are expected to help solve many of today's global challenges, revolutionizing several fields such as computing, sensing and secure communications. In this regard, the need for precise manipulation of the dynamics of a quantum system and its optimization has given rise to the field of quantum control theory. In the search for optimal controls, accurate derivatives are a possible method to traverse and ultimately converge in quantum optimization landscapes. In this work we study an efficient algorithm for computing analytically-exact derivatives by formulating the control problem in the basis that diagonalizes the control Hamiltonian and applying a specific Trotterized time propagation scheme. The method is numerically verified for a system of superconducting transmon qubits in the few- and many body regime using matrix product states. The comparison between the results obtained using an exact dynamics via Krylov subspace methods shows how the approximate dynamics ultimately sets a trade-off between computational complexity and quality of the final solutions

Test-Sheet Composition Using Analytic Hierarchy Process and Hybrid Metaheuristic Algorithm TS/BBO

Due to the shortcomings in the traditional methods which dissatisfy the examination requirements in composing test sheet, a new method based on tabu search (TS) and biogeography-based optimization (BBO) is proposed. Firstly, according to the requirements of the test-sheet composition such as the total score, test time, chapter score, knowledge point score, question type score, cognitive level score, difficulty degree, and discrimination degree, a multi constrained multiobjective model of test-sheet composition is constructed. Secondly, analytic hierarchy process (AHP) is used to work out the weights of all the test objectives, and then the multiobjective model is turned into the single objective model by the linear weighted sum. Finally, an improved biogeography-based optimization—TS/BBO is proposed to solve test-sheet composition problem. To prove the performance of TS/BBO, TS/BBO is compared with BBO and other population-based optimization methods such as ACO, DE, ES, GA, PBIL, PSO, and SGA. The experiment illustrates that the proposed approach can effectively improve composition speed and success rate