Optimal placement and sizing of distributed generation for loss minimization using ABC optimization

The distributed generation (DG) refers to the use of the nearby units of small generation or in the sleefelzentres. Revisions have shown that unfitting choice of the position and scope of the DG can lead to bigger system sufferers than DG's sufferers. Public services are already available before the high loss of energy loss and of the mediocre voltage profile, in particular developing countries cannot tolerate any increase in losses. From optimal allocation, public services in the reduction of system losses improve tension adjustment and improve delivery reliability. This article aims to minimalize the annual system's annual loss through the appropriate positioning and size of the DG units. The artificials bee colony (ABC) of EPA are inspired by the behavior of the API feeding, this method is incredibly conventional, a stroke or a peoplated stochastic optimization algorithm to meet the solution of the specified problem. At MATLAB, a probabilistic approach is simulated to reach the target indicates that the size of the DGs to be installed to reduce almost the same loss as a percentage that the situation in which the load power is considered constant.

Online convex combination of ranking models

As a task of high importance for recommender systems, we consider the problem of learning the convex combination of ranking algorithms by online machine learning. First, we propose a stochastic optimization algorithm that uses finite differences. Our new algorithm achieves close to optimal empirical performance for two base rankers, while scaling well with an increased number of models. In our experiments with five real-world recommendation data sets, we show that the combination offers significant improvement over previously known stochastic optimization techniques. The proposed algorithm is the first effective stochastic optimization method for combining ranked recommendation lists by online machine learning. Secondly, we propose an exponentially weighted algorithm based on a grid over the space of combination weights. We show that the algorithm has near-optimal worst-case performance bound. The bound provides the first theoretical guarantee for non-convex bandits using limited number of evaluations under very general conditions.

Multiscale nonlinear inversion of gravity data for depth-to-basement estimation via coupled stochastic-deterministic optimization

We have developed a multiscale approach for solving 2D and 3D nonlinear inverse problems of gravity data in estimating the basement topography. The inversion is carried out in two stages in which the long-wavelength features of the basement are first estimated from smoothed gravity data via a stochastic optimization algorithm. The solution of this stage is used as the starting model for a deterministic optimization algorithm to reconstruct the short-wavelength features from the full-spectrum gravity data. The forward problem is capable of handling lateral and vertical variations in the density of sediments. Two cases are considered regarding prior knowledge about the density: (1) The density contrast between sediments at the surface and the underlying basement and its vertical variations are a priori known, and (2) only the density contrast at the surface is known with its vertical gradient to be recovered in the inversion. In the former case, the unknowns of the problem are the depths, whereas in the latter case, they are the depths and density gradients defined individually for each prism. Therefore, the inverse problem is ill-posed and has many local minima. The stochastic optimization algorithm uses a random initial model and estimates a coarse model of the basement topography. By repeating the stochastic inversion, an ensemble of solutions is formed defining an equivalent domain in the model space supposed to be within the neighborhood of the global minimum of which several starting solutions are extracted for the secondary deterministic inversion. The presented methodology has been tested successfully in converging to the global minima in 2D and 3D cases with 50 and 2352 total number of prisms, respectively. Finally, the inversion algorithm is used to calculate the thickness of the sediments in the South Caspian Basin using the EIGEN-6c4 global gravity model.

Conjugate Direction Particle Swarm Optimization Based Approach to Determine Kinetic Parameters from Part of Adiabatic Data

Due to the limited detection range of the adiabatic equipment, it is difficult to get complete experimental curve of some materials and calculate the kinetic parameters. In this work, the conjugate direction particle swarm optimization (CDPSO) approach, as a global stochastic optimization algorithm, is proposed to estimate the kinetic parameters and complete experimental curve from part of adiabatic calorimetric data. This algorithm combines the conjugate direction algorithm (CD) which has the ability to escape from the local extremum and the global optimization ability of the particle swarm optimization (PSO) which finds the globally optimal solutions. One case was used to verify this method: 20% DTBP in toluene decompositions under adiabatic conditions. Comparing the experimental and calculated complete temperature curve, the accuracy of the fitted kinetic parameters calculated by no less than 70% temperature rise rate proportion of data is verified. The value of TD24 is well-deviated even used 10% proportion of data. The case reasonably proves the effectiveness of CDPSO algorithm in the estimation of kinetic parameters from part of adiabatic data.

An enhanced particle swarm optimization algorithm

<p>In this paper, an enhanced stochastic optimization algorithm based on the basic Particle Swarm Optimization (PSO) algorithm is proposed. The basic PSO algorithm is built on the activities of the social feeding of some animals. Its parameters may influence the solution considerably. Moreover, it has a couple of weaknesses, for example, convergence speed and premature convergence. As a way out of the shortcomings of the basic PSO, several enhanced methods for updating the velocity such as Exponential Decay Inertia Weight (EDIW) are proposed in this work to construct an Enhanced PSO (EPSO) algorithm. The suggested algorithm is numerically simulated established on five benchmark functions with regards to the basic PSO approaches. The performance of the EPSO algorithm is analyzed and discussed based on the test results.</p>

Parallel Simultaneous Perturbation Optimization

Stochastic computer simulations enable users to gain new insights into complex physical systems. Optimization is a common problem in this context: users seek to find model inputs that maximize the expected value of an objective function. The objective function, however, is time-intensive to evaluate, and cannot be directly measured. Instead, the stochastic nature of the model means that individual realizations are corrupted by noise. More formally, we consider the problem of optimizing the expected value of an expensive black-box function with continuously-differentiable mean, from which observations are corrupted by Gaussian noise. We present parallel simultaneous perturbation optimization (PSPO), which extends a well-known stochastic optimization algorithm, simultaneous perturbation stochastic approximation, in several important ways. Our modifications allow the algorithm to fully take advantage of parallel computing resources, like high-performance cloud computing. The resulting PSPO algorithm takes fewer time-consuming iterations to converge, automatically chooses the step size, and can vary the error tolerance by step. Theoretical results are supported by a numerical example.