HEURISTIC OPTIMIZATION OF THE FOUNDATION OF A DYNAMICALLY STRESSED ROTATING MACHINE USING THE LATE ACCEPTANCE HILL CLIMBING (LAHC) ALGORITHM

10.6036/9762 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 498-504
Author(s):  
JUAN LUIS TERRADEZ MARCO ◽  
ANTONIO HOSPITALER PEREZ ◽  
VICENTE ALBERO GABARDA
Keyword(s):  
Cost Function ◽  
Random Search ◽  
Optimal Solution ◽  
Hill Climbing ◽  
Dynamic Loads ◽  
Rotating Machine ◽  
Permanent Working ◽  
The Cost ◽  
Late Acceptance Hill Climbing

This paper proposal the optimization of a foundation for rotative machine under dynamic loads in transient and permanent working mode. The foundation depends on fix parameters and 37 variables. Functional constraints are defined for the foundation. A cost function depending on the variables is defined to be minimized to find the optimal. From all the possible solutions, only are selected the ones that validate the constrains and minimize the cost function. The search of the optimal solution is made with an algorithm of random search by “neighbouring of one point” called Last Acceptance Hill Climbing(LAHC). It is an algorithm of the type called “Adaptative Memory Programming” (AMP) that accepts worse solutions to get the local minimum and learns of the results of the search. The algorithm only depends on the length of the comparison vector and the stop criteria. 350 experiences were made with different length of the comparison vector. It was analysed the quality of the optimal solutions got it with each length of the vector. Quality of the set of solutions was compared fitting them to a 3 parameters Weibull distribution.

scholarly journals HapSolo: an optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Edwin A. Solares ◽  
Yuan Tao ◽  
Anthony D. Long ◽  
Brandon S. Gaut
Keyword(s):  
Cost Function ◽  
Anopheles Funestus ◽  
Hill Climbing ◽  
Optimization Approach ◽  
Sequencing Technology ◽  
Genome Data ◽  
A Genome ◽  
Long Read ◽  
Downstream Analysis ◽  
The Cost

Abstract Background Despite marked recent improvements in long-read sequencing technology, the assembly of diploid genomes remains a difficult task. A major obstacle is distinguishing between alternative contigs that represent highly heterozygous regions. If primary and secondary contigs are not properly identified, the primary assembly will overrepresent both the size and complexity of the genome, which complicates downstream analysis such as scaffolding. Results Here we illustrate a new method, which we call HapSolo, that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics. HapSolo evaluates candidate primary assemblies using BUSCO scores and then distinguishes among candidate assemblies using a cost function. The cost function can be defined by the user but by default considers the number of missing, duplicated and single BUSCO genes within the assembly. HapSolo performs hill climbing to minimize cost over thousands of candidate assemblies. We illustrate the performance of HapSolo on genome data from three species: the Chardonnay grape (Vitis vinifera), with a genome of 490 Mb, a mosquito (Anopheles funestus; 200 Mb) and the Thorny Skate (Amblyraja radiata; 2650 Mb). Conclusions HapSolo rapidly identified candidate assemblies that yield improvements in assembly metrics, including decreased genome size and improved N50 scores. Contig N50 scores improved by 35%, 9% and 9% for Chardonnay, mosquito and the thorny skate, respectively, relative to unreduced primary assemblies. The benefits of HapSolo were amplified by down-stream analyses, which we illustrated by scaffolding with Hi-C data. We found, for example, that prior to the application of HapSolo, only 52% of the Chardonnay genome was captured in the largest 19 scaffolds, corresponding to the number of chromosomes. After the application of HapSolo, this value increased to ~ 84%. The improvements for the mosquito’s largest three scaffolds, representing the number of chromosomes, were from 61 to 86%, and the improvement was even more pronounced for thorny skate. We compared the scaffolding results to assemblies that were based on PurgeDups for identifying secondary contigs, with generally superior results for HapSolo.

scholarly journals Mathematical modeling for a multiproduct manufacturing system featuring postponement, external suppliers, overtime, and scrap

2022 ◽  
Vol 13 (1) ◽  
pp. 1-12
Author(s):  
Yuan-Shyi P. Chiu ◽  
Jian-Hua Lian ◽  
Victoria Chiu ◽  
Yunsen Wang ◽  
Hsiao-Chun Wu
Keyword(s):  
Decision Making ◽  
Cost Function ◽  
Due Dates ◽  
Numerical Illustration ◽  
Screening Process ◽  
Multiproduct Manufacturing ◽  
The Cost ◽  
Decision Making Model ◽  
Mathematical Derivation

Manufacturing firms operating in today’s competitive global markets must continuously find the appropriate manufacturing scheme and strategies to effectively meet customer needs for various types of quality of merchandise under the constraints of short order lead-time and limited in-house capacity. Inspired by the offering of a decision-making model to aid smooth manufacturers’ operations, this study builds an analytical model to expose the influence of the outsourcing of common parts, postponement policies, overtime options, and random scrapped items on the optimal replenishment decision and various crucial system performance indices of the multiproduct problem. A two-stage fabrication scheme is presented to handle the products’ commonality and the uptime-reduced strategies to satisfy the short amount of time before the due dates of customers’ orders. A screening process helps identify and remove faulty items to ensure the finished lot’s anticipated quality. Mathematical derivation assists us in finding the manufacturing relevant total cost function. The differential calculus helps optimize the cost function and determine the optimal stock-replenishing rotation cycle policy. Lastly, a simulated numerical illustration helps validate our research result’s applicability and demonstrate the model’s capability to disclose the crucial managerial insights and facilitate manufacturing-relevant decision making.

scholarly journals Solar Simulation Model to Find Optimal Solution for Domestic Load

2021 ◽  
Vol 9 (VII) ◽  
pp. 2643-2646
Author(s):  
Mujeeb Jeelani Magray
Keyword(s):  
Feasible Solution ◽  
Optimal Solution ◽  
Power Production ◽  
Solar Pv ◽  
Environment Friendly ◽  
Power Outage ◽  
The World ◽  
Cost Competitiveness ◽  
The Cost

Looking at the energy sector of India, it is the 3rd largest producer in the world and holds the largest grid in the world. Despite the presence of largest grid, still frequent power outage, delivery of low quality of power and unreliability of supply persistent in some areas. This unreliable power supply creates hindrance in the overall development of the region. On the other hand solar power is most widely used source of renewable energy, can be able to provide feasible solution. By installation of solar PV array in the premises of the load center the reliability will be increased. The power production from the PV is also cost competitive and environment friendly. As solar PV power is intermittent in nature, the most old and mature storage technology ,i.e. battery can be integrated to mitigate this intermittency nature. The present study examines the feasibility and optimizes the size of such system, while the cost competitiveness is kept at the center.

scholarly journals Selection model of work technology based on multi–criteria evaluations

2020 ◽  
Vol 164 ◽  
pp. 08030
Author(s):  
Sergey Barkalov ◽  
Pavel Kurochka ◽  
Anton Khodunov ◽  
Natalia Kalinina
Keyword(s):  
Cost Function ◽  
Optimal Solution ◽  
Budget Constraint ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Customer Preferences ◽  
Optimal Solution Set ◽  
The Cost ◽  
Comprehensive Indicator ◽  
Selection Of

A model for the selection of options for the production of work in a construction project is considered, when each option is characterized by a set of criteria. The number of analyzed options is being reduced based on the construction of the Pareto-optimal solution set. The remaining options are used to solve the problem based on the network model,\ in which the solution will be a subcritical path that meets budgetary constraints. At the same time, the proposed comprehensive indicator characterizing the preferences of the customer makes it possible to determine alternative options for performing work in the energy project in such a way that the amount of costs allocated to implement the set of work under consideration is minimal. Another statement of the problem is also considered when it is necessary to determine a strategy for the implementation of an energy project that, given a planned budget constraint, maximizes the growth of a comprehensive indicator that characterizes customer preferences in this project. The solution of the tasks is given under the assumption of the convexity of the cost function.

scholarly journals Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)

2018 ◽  
Vol 11 (12) ◽  
pp. 4739-4754 ◽  
Author(s):  
Vladislav Bastrikov ◽  
Natasha MacBean ◽  
Cédric Bacour ◽  
Diego Santaren ◽  
Sylvain Kuppel ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cost Function ◽  
Land Surface ◽  
Random Search ◽  
Single Site ◽  
Model Data ◽  
Parameter Optimisation ◽  
Gradient Based ◽  
The Difference ◽  
The Cost

Abstract. Land surface models (LSMs), which form the land component of earth system models, rely on numerous processes for describing carbon, water and energy budgets, often associated with highly uncertain parameters. Data assimilation (DA) is a useful approach for optimising the most critical parameters in order to improve model accuracy and refine future climate predictions. In this study, we compare two different DA methods for optimising the parameters of seven plant functional types (PFTs) of the ORCHIDEE LSM using daily averaged eddy-covariance observations of net ecosystem exchange and latent heat flux at 78 sites across the globe. We perform a technical investigation of two classes of minimisation methods – local gradient-based (the L-BFGS-B algorithm, limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm with bound constraints) and global random search (the genetic algorithm) – by evaluating their relative performance in terms of the model–data fit and the difference in retrieved parameter values. We examine the performance of each method for two cases: when optimising parameters at each site independently (“single-site” approach) and when simultaneously optimising the model at all sites for a given PFT using a common set of parameters (“multi-site” approach). We find that for the single site case the random search algorithm results in lower values of the cost function (i.e. lower model–data root mean square differences) than the gradient-based method; the difference between the two methods is smaller for the multi-site optimisation due to a smoothing of the cost function shape with a greater number of observations. The spread of the cost function, when performing the same tests with 16 random first-guess parameters, is much larger with the gradient-based method, due to the higher likelihood of being trapped in local minima. When using pseudo-observation tests, the genetic algorithm results in a closer approximation of the true posterior parameter value in the L-BFGS-B algorithm. We demonstrate the advantages and challenges of different DA techniques and provide some advice on using it for the LSM parameter optimisation.

scholarly journals The cost function of the data fusion process and its application

2019 ◽  
Vol 12 (5) ◽  
pp. 2967-2977 ◽  
Author(s):  
Simone Ceccherini ◽  
Nicola Zoppetti ◽  
Bruno Carli ◽  
Ugo Cortesi ◽  
Samuele Del Bianco ◽  
...  
Keyword(s):  
Data Fusion ◽  
Cost Function ◽  
Covariance Matrix ◽  
Expected Value ◽  
Covariance Matrices ◽  
Complete Data ◽  
Sufficient Statistics ◽  
The Cost ◽  
Analytical Expressions

Abstract. When the complete data fusion method is used to fuse inconsistent measurements, it is necessary to add to the measurement covariance matrix of each fusing profile a covariance matrix that takes into account the inconsistencies. A realistic estimate of these inconsistency covariance matrices is required for effectual fused products. We evaluate the possibility of assisting the estimate of the inconsistency covariance matrices using the value of the cost function minimized in the complete data fusion. The analytical expressions of expected value and variance of the cost function are derived. Modelling the inconsistency covariance matrix with one parameter, we determine the value of the parameter that makes the reduced cost function equal to its expected value and use the variance to assign an error to this determination. The quality of the inconsistency covariance matrix determined in this way is tested for simulated measurements of ozone profiles obtained in the thermal infrared in the framework of the Sentinel-4 mission of the Copernicus programme. As expected, the method requires sufficient statistics and poor results are obtained when a small number of profiles are being fused together, but very good results are obtained when the fusion involves a large number of profiles.

scholarly journals Automatic Registration and Error Detection of Multiple Slices Using Landmarks

2001 ◽  
Vol 23 (3-4) ◽  
pp. 159-165
Author(s):  
Hans Frimmel ◽  
Lars Egevad ◽  
Christer Busch ◽  
Ewert Bengtsson
Keyword(s):  
Cost Function ◽  
Error Detection ◽  
3D Structure ◽  
Automatic Registration ◽  
Registration Method ◽  
Automated Method ◽  
Current State ◽  
Manual Registration ◽  
The Cost

Objectives. When analysing the 3D structure of tissue, serial sectioning and staining of the resulting slices is sometimes the preferred option. This leads to severe registration problems. In this paper, a method for automatic registration and error detection of slices using landmark needles has been developed. A cost function takes some parameters from the current state of the problem to be solved as input and gives a quality of the current solution as output. The cost function used in this paper, is based on a model of the slices and the landmark needles. The method has been used to register slices of prostates in order to create 3D computer models. Manual registration of the same prostates has been undertaken and compared with the results from the algorithm. Methods. Prostates from sixteen men who underwent radical prostatectomy were formalin fixed with landmark needles, sliced and the slices were computer reconstructed. The cost function takes rotation and translation for each prostate slice, as well as slope and offset for each landmark needle as input. The current quality of fit of the model, using the input parameters given, is returned. The function takes the built‐in instability of the model into account. The method uses a standard algorithm to optimize the prostate slice positions. To verify the result, s standard method in statistics was used. Results. The methods were evaluated for 16 prostates. When testing blindly, a physician could not determine whether the registration shown to him were created by the automated method described in this paper, or manually by an expert, except in one out of 16 cases. Visual inspection and analysis of the outlier confirmed that the input data had been deformed. The automatic detection of erroneous slices marked a few slices, including the outlier, as suspicious. Conclusions. The model based registration performs better than traditional simple slice‐wise registration. In the case of prostate slice registration, other aspects, such as the physical slicing method used, may be more important to the final result than the selection of registration method to use.

scholarly journals The cost function of the data fusion process and its application

2019 ◽  
Author(s):  
Simone Ceccherini ◽  
Nicola Zoppetti ◽  
Bruno Carli ◽  
Ugo Cortesi ◽  
Samuele Del Bianco ◽  
...  
Keyword(s):  
Data Fusion ◽  
Cost Function ◽  
Covariance Matrix ◽  
Expected Value ◽  
Covariance Matrices ◽  
Complete Data ◽  
Sufficient Statistics ◽  
The Cost ◽  
Analytical Expressions

Abstract. When the complete data fusion method is used to fuse inconsistent measurements, it is necessary to add to the measurement covariance matrix of each fusing profile a covariance matrix that takes into account the inconsistencies. A realistic estimate of these inconsistency covariance matrices is required for effectual fused products. We evaluate the possibility of assisting the estimate of the inconsistency covariance matrices using the value of the cost function minimized in the complete data fusion. The analytical expressions of expected value and variance of the cost function are derived. Modelling the inconsistency covariance matrix with one parameter, we determine the value of the parameter that makes the reduced cost function equal to its expected value and use the variance to assign an error to this determination. The quality of the inconsistency covariance matrix determined in this way is tested for simulated measurements of ozone profiles obtained in the thermal infrared in the framework of the Sentinel 4 mission of the Copernicus programme. As expected, the method requires a sufficient statistics and poor results are obtained when a small numbers of profiles are being fused together, but very good results are obtained when the fusion involves a large number of profiles.

scholarly journals On Generating Optimal Signal Probabilities for Random Tests: A Genetic Approach

VLSI Design ◽  
1996 ◽  
Vol 4 (3) ◽  
pp. 207-215 ◽  
Author(s):  
M. Srinivas ◽  
L. M. Patnaik
Keyword(s):  
Genetic Algorithms ◽  
Cost Function ◽  
Gradient Descent ◽  
Random Search ◽  
Search Space ◽  
Optimization Techniques ◽  
Gradient Descent Methods ◽  
Test Vectors ◽  
The Cost ◽  
Optimal Signal

Genetic Algorithms are robust search and optimization techniques. A Genetic Algorithm based approach for determining the optimal input distributions for generating random test vectors is proposed in the paper. A cost function based on the COP testability measure for determining the efficacy of the input distributions is discussed. A brief overview of Genetic Algorithms (GAs) and the specific details of our implementation are described. Experimental results based on ISCAS-85 benchmark circuits are presented. The performance of our GAbased approach is compared with previous results. While the GA generates more efficient input distributions than the previous methods which are based on gradient descent search, the overheads of the GA in computing the input distributions are larger.To account for the relatively quick convergence of the gradient descent methods, we analyze the landscape of the COP-based cost function. We prove that the cost function is unimodal in the search space. This feature makes the cost function amenable to optimization by gradient-descent techniques as compared to random search methods such as Genetic Algorithms.

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