DHIMAN: A novel algorithm for economic Dispatch problem based on optimization metHod usIng Monte Carlo simulation and Astrophysics coNcepts

2019 ◽  
Vol 34 (04) ◽  
pp. 1950032 ◽  
Author(s):  
Gaurav Dhiman ◽  
Pritpal Singh ◽  
Harsimran Kaur ◽  
Ritika Maini
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
State Of The Art ◽  
Real Life ◽  
Optimization Method ◽  
Optimization Approach ◽  
Effective Number ◽  
Number Of Solutions ◽  
Efficient Prediction ◽  
Competing Models

This paper presents a new model using optimization approach for efficient prediction of load in real-life environment. Monte Carlo simulation and Schrödinger equations provide the effective number of solutions. This technique is useful in representation of relationships between different models. The proposed algorithm is verified and validated with various state-of-the-art approaches for solving economic load power dispatch problem to demonstrate its efficiency. Experimental results signify that the proposed algorithm is more precise than existing competing models.

The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development

2020 ◽  
Vol 20 (14) ◽  
pp. 1389-1402 ◽  
Author(s):  
Maja Zivkovic ◽  
Marko Zlatanovic ◽  
Nevena Zlatanovic ◽  
Mladjan Golubović ◽  
Aleksandar M. Veselinović
Keyword(s):  
Monte Carlo ◽  
Molecular Docking ◽  
Computer Calculation ◽  
Molecular Graph ◽  
Optimization Method ◽  
Docking Studies ◽  
Optimization Approach ◽  
Qsar Modeling ◽  
Monte Carlo Optimization ◽  
Molecular Docking Studies

In recent years, one of the promising approaches in the QSAR modeling Monte Carlo optimization approach as conformation independent method, has emerged. Monte Carlo optimization has proven to be a valuable tool in chemoinformatics, and this review presents its application in drug discovery and design. In this review, the basic principles and important features of these methods are discussed as well as the advantages of conformation independent optimal descriptors developed from the molecular graph and the Simplified Molecular Input Line Entry System (SMILES) notation compared to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results from Monte Carlo optimization-based QSAR modeling with the further addition of molecular docking studies applied for various pharmacologically important endpoints. SMILES notation based optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/ decrease of biological activity, which are used further to design compounds with targeted activity based on computer calculation, are presented. In this mini-review, research papers in which molecular docking was applied as an additional method to design molecules to validate their activity further, are summarized. These papers present a very good correlation among results obtained from Monte Carlo optimization modeling and molecular docking studies.

scholarly journals Social-network-based water supply network sectorization methodology using monte carlo simulation to predict economical and operational benefits

2017 ◽  
Vol 26 ◽  
pp. 44-53
Author(s):  
Enrique Campbell ◽  
Amilkar Illaya-Ayza ◽  
Joaquín Izquierdo ◽  
Rafael Pérez-García ◽  
Idel Montalvo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Water Supply ◽  
Optimization Method ◽  
Supply Network ◽  
Water Supply Network ◽  
Water Supply Management ◽  
Net Profit ◽  
Short Run ◽  
The Impact

Water Supply Network (WSN) sectorization is a broadly known technique aimed at enhancing water supply management. In general, existing methodologies for sectorization of WSNs are limited to assessment of the impact of its implementation over reduction of background leakage, underestimating increased capacity to detect new leakage events and undermining appropriate investment substantiation. In this work, we raise this issue and put in place a methodology to optimize sectors' design. To this end, we carry out a novel combination of the Short Run Economic Leakage Level concept (SRELL- corresponding to leakage level that can occur in a WSN in a certain period of time and whose reparation would be more costly than the benefits that can be obtained). With a non-deterministic optimization method based on Genetic Algorithms (GAs) in combination with Monte Carlo simulation. As an example of application, methodology is implemented over a 246 km pipe-long WSN, reporting 72 397 $/year as net profit.

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

scholarly journals Thomson linac-based X-ray generator: a primer for theory and design

2016 ◽  
Vol 34 (4) ◽  
pp. 637-644 ◽  
Author(s):  
I.A. Artyukov ◽  
E.G. Bessonov ◽  
M.V. Gorbunkov ◽  
Y.Y. Maslova ◽  
N.L. Popov ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Laser Radiation ◽  
Conceptual Design ◽  
Large Scale ◽  
State Of The Art ◽  
Thomson Scattering ◽  
Powerful Laser ◽  
X Ray ◽  
X Ray Imaging

AbstractThe paper presents a general theoretical framework and related Monte Carlo simulation of novel type of the X-ray sources based on relativistic Thomson scattering of powerful laser radiation. Special attention is paid to the linac X-ray generators by way of two examples: conceptual design for production of 12.4 keV photons and presently operating X-ray source of 29.4 keV photons. Our analysis shows that state-of-the-art laser and accelerator technologies enable to build up a compact linac-based Thomson source for the same X-ray imaging and diffraction experiments as in using of a large-scale X-ray radiation facility like a synchrotron or Thomson generator based on electron storage ring.

scholarly journals Robustness Test of Selected Estimators of Linear Regression with Autocorrelated Error Term: A Monte-Carlo Simulation Study

2021 ◽  
pp. 1-17
Author(s):  
Rauf Ibrahim Rauf ◽  
Okoli Juliana Ifeyinwa ◽  
Haruna Umar Yahaya
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Linear Regression ◽  
Simulation Study ◽  
Explanatory Variable ◽  
Real Life ◽  
Least Square ◽  
Monte Carlo Simulation Study ◽  
Error Terms

Assumptions in the classical linear regression model include that of lack of autocorrelation of the error terms and the zero covariance between the explanatory variable and the error terms. This study is channeled towards the estimation of the parameters of the linear models for both time series and cross-sectional data when the above two assumptions are violated. The study used the Monte-Carlo simulation method to investigate the performance of six estimators: ordinary least square (OLS), Prais-Winsten (PW), Cochrane-Orcutt (CC), Maximum Likelihood (MLE), Restricted Maximum- Likelihood (RMLE) and the Weighted Least Square (WLS) in estimating the parameters of a single linear model in which the explanatory variable is also correlated with the autoregressive error terms. Using the models’ finite properties(mean square error) to measure the estimators’ performance, the results shows that OLS should be preferred when autocorrelation level is relatively mild (ρ = 0.3) and the PW, CC, RMLE, and MLE estimator will perform better with the presence of any level of AR (1) disturbance between 0.4 to 0.8 level, while WLS shows better performance at 0.9 level of autocorrelation and above. The study thus recommended the application of the various estimators considered to real-life data to affirm the results of this simulation study.

scholarly journals Simulation of Bell Correlations in Walker Systems

2018 ◽  
Author(s):  
Marc Fleury
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Local Field ◽  
Real Life ◽  
Bell Inequalities ◽  
Weak Field ◽  
Physical Form ◽  
Bell Violation ◽  
Non Local ◽  
Space Separation

We describe results from a Monte-Carlo simulation of Bell-CHSH type correlations in hydrodynamic walkers. We study feasibility of a real life walker test with relevant hydrodynamic parametric ranges. We observe the generic formation of pairs of walkers strongly anti-correlated both in position and momentum. With this source of entangled walkers, we model the insertion of 2 pins in the bath as a notion of measure, akin to the polarizers of photonic Bell tests. This insertion of pins, either static or dynamic, introduces 2 weak field signals. Each field has the physical form of a standing wave Bessel hat, representing the non-local (field mediated) influences of the measure on the walkers. With this representation of the measure, we develop protocol for a Bell game with actual hydrodynamic walkers. We model both static and dynamic insertion of pins in the walker bath. Static pins give us numerical S > 2, as a permissible Bell violation for a non-local (field based) effect. Dynamic insertion of the pins, however, leads to causal space separation of the two arms. We observe the again expected S ≤ 2. We argue for the hydrodynamic implementation and observation of these effects as a walker visualization of Bell inequalities.

Optimization-Based Probabilistic Consequence Scenario Construction for Lifeline Systems

2014 ◽  
Vol 30 (4) ◽  
pp. 1531-1551 ◽  
Author(s):  
Jared Gearhart ◽  
Nathanael Brown ◽  
Dean Jones ◽  
Linda Nozick ◽  
Natalia Romero ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Joint Distribution ◽  
Optimization Method ◽  
Seismic Zone ◽  
Realistic Case ◽  
Lifeline System ◽  
Highway Network ◽  
Lifeline Systems

The construction of a suite of consequence scenarios that is consistent with the joint distribution of damage to a lifeline system is critical to properly estimating regional loss after an earthquake. This paper describes an optimization method that identifies a suite of consequence scenarios that can be used in regional loss estimation for lifeline systems when computational demands are of concern, and it is important to capture the spatial correlation associated with individual events. This method is applied to a realistic case study focused on the highway network in Memphis, Tennessee, within the New Madrid Seismic Zone. This case study illustrates that significantly fewer consequence scenarios are needed with this method than would be required using Monte Carlo simulation.

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