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Full-waveform inversion (FWI) utilizes optimization methods to recover an optimal Earth model to best fit the observed seismic record in a sense of a predefined norm. Since FWI combines mathematic inversion and full-wave equations, it has been recognized as one of the key methods for seismic data imaging and Earth model building in the fields of global/regional and exploration seismology. Unfortunately, conventional FWI fixes background velocity mainly relying on refraction and turning waves that are commonly rich in large offsets. By contrast, reflections in the short offsets mainly contribute to the reconstruction of the high-resolution interfaces. Restricted by acquisition geometries, refractions and turning waves in the record usually have limited penetration depth, which may not reach oil/gas reservoirs. Thus, reflections in the record are the only source that carries the information of these reservoirs. Consequently, it is meaningful to develop reflection-waveform inversion (RWI) that utilizes reflections to recover background velocity including the deep part of the model. This review paper includes: analyzing the weaknesses of FWI when inverting reflections; overviewing the principles of RWI, including separation of the tomography and migration components, the objective functions, constraints; summarizing the current status of the technique of RWI; outlooking the future of RWI.

We study regression M -estimates in the setting where p , the number of covariates, and n , the number of observations, are both large, but [Formula]. We find an exact stochastic representation for the distribution of [Formula] at fixed p and n under various assumptions on the objective function ρ and our statistical model. A scalar random variable whose deterministic limit [Formula] can be studied when [Formula] plays a central role in this representation. We discover a nonlinear system of two deterministic equations that characterizes [Formula]. Interestingly, the system shows that [Formula] depends on ρ through proximal mappings of ρ as well as various aspects of the statistical model underlying our study. Several surprising results emerge. In particular, we show that, when [Formula] is large enough, least squares becomes preferable to least absolute deviations for double-exponential errors.

In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current solutions. The algorithm identifies and uses key neurons in the hidden layer that correspond to the highest objective function values to generate new solutions. The centroids and standard deviations of these neurons guide the sampling process, which continues until the desired number of solutions is reached. By focusing on the areas of the search space that yield high objective function values, the algorithm avoids exhaustive solution evaluations and significantly reduces the number of function evaluations. The effectiveness of the method is demonstrated through a comparison with popular metaheuristic algorithms across several test functions, where it consistently outperforms existing techniques, delivers higher-quality solutions, and improves convergence rates.

We consider, in the modern setting of high-dimensional statistics, the classic problem of optimizing the objective function in regression using M-estimates when the error distribution is assumed to be known. We propose an algorithm to compute this optimal objective function that takes into account the dimensionality of the problem. Although optimality is achieved under assumptions on the design matrix that will not always be satisfied, our analysis reveals generally interesting families of dimension-dependent objective functions.

Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

In this paper, we address a composite optimization problem in which the objective function consists of two terms: the first presents a function with a gradient that satisfies a Lipschitz–Hölder composition, while the second one is a convex function. Under general settings, we propose and analyze a new coordinate descent method that can operate without the use of derivatives. The algorithm is an adaptation of the coordinate proximal gradient method, specifically designed to consider the composite form of the objective function. We perform a complete worst-case complexity analysis, assuming that the coordinates (or blocks of coordinates) are selected in a cyclic manner. In addition, we present academic numerical examples that illustrate the efficiency of our algorithm in practical problems.

We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the no-short-positions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio.

The effective and efficient optimization of Storm Water Management Model (SWMM) parameters is critical to improving the accuracy of the urban rainfall-runoff simulation. Therefore, it is necessary to investigate the applicability of the dynamic system response curve (DSRC) method in optimizing SWMM model parameters, which is newly proposed to solve the nonlinear problems encountered by current widely used optimization methods. A synthetic case, free of data and model errors, was used to examine the applicability of the DSRC with single-objective or multi-objective functions in finding the optimum parameter values known by assumption. A real watershed case was selected for the optimization of SWMM parameters by use of DSRC with the most suitable objective function, which was determined by a synthetic case. In addition, the advantages of the DSRC in SWMM parameter optimization over the Particle Swarm Optimization(PSO) and Multiple Objective Particle Swarm Optimization(MOPSO) algorithms were analyzed in terms of NSE, REv, REp, and EPt. The results revealed that the DSRC with multi-objective function could find the global optima of all SWMM model parameters in the synthetic case, but it could only attain part of them with a single-objective function. In the real watershed case, the DSRCS-optimized SWMM performed better than MOPSO-optimized one with an increase of average NSE by 5.8% and a reduction of average REv, REp and EPt by -53.7%, -67.9%, and -34.6% respectively during the study period. The outputs of this paper could provide a promising approach for the optimization of SWMM parameters and the improvement of urban flooding simulation accuracy, and a scientific support for urban flood risk control and mitigation.

ABSTRACT In this article, a multi‐objective comprehensive evaluation method is established by comprehensively considering the power and shaft diameter of a multistage hydraulic turbine with an ultrahigh water head and low flow rate to utilize water energy efficiently. Using this method, several schemes for calculating the runner's geometric parameters are attained through the scheme design of different maximum numbers of stages and rotational speeds under different operating conditions of water pressure and flow rate. The reasonable schemes are determined by the maximum value in the intersection of runner diameter value ranges, the blade inlet angle β1 ≥ 12° and the blade inlet flow angle α1 ≥ 6°. Based on the multi‐objective function of water energy utilization considering the comprehensive performance of the runner diameter and power, the design parameters and design stage numbers of the multistage hydraulic turbine with the optimal comprehensive performance of power and shaft diameter are obtained. This method is recommended for the design of ultra‐low specific speed multistage hydraulic turbines with a specific speed of less than 50. Multi‐objective evaluation method for efficient water energy utilization in multistage hydraulic turbines with ultra‐high water head and low flow rate

Internet of things establishes communication among heterogeneous devices. IoT network is low power and lossy network known as LLN. The components in LLN use low power for its operations. The Internet Engineering Task Force (IETF) has defined routing protocol for standardized LLN, i.e., routing protocol for low-power and lossy networks (RPL). One of the major challenges in RPL is efficient conservation of node energy to improve the life of the LLN network. In the RPL network, most of the energy is consumed while regulating and controlling the packets rather than transmission. The algorithm used for regulating and controlling packet in RPL is called trickle timer algorithm. Hence to improve the lifetime of network it is essential to modify the existing trickle timer algorithm. In this paper, we have proposed a new algorithm called EE-trickle. The performance of EE-trickle is compared with existing trickle using the simulator Cooja and using open test bed of future Internet of things lab. From the experiments, it is identified that EE-trickle provides better PDR along with less energy consumption than the existing trickle. Hence, the paper helps the future researchers who work on energy consumption in RPL to make use of EE-trickle in their experiment rather than existing trickle.