Wyniki wyszukiwania - BazTech

1

Optimizing the Job Shop Scheduling Problem with a no Wait Constraint by Using the Jaya Algorithm Approach

Bougloula Aimade Eddine

Management and Production Engineering Review

|

2023

|

Vol. 14, No. 3

148--155

EN

This work is interested to optimize the job shop scheduling problem with a no wait constraint. This constraint occurs when two consecutive operations in a job must be processed without any waiting time either on or between machines. The no wait job shop scheduling problem is a combinatorial optimization problem. Therefore, the study presented here is focused on solving this problem by proposing strategy for making Jaya algorithm applicable for handling optimization of this type of problems and to find processing sequence that minimizes the makespan (Cmax). Several benchmarks are used to analyze the performance of this algorithm compared to the best-known solutions.

2

Size and shape design optimization of truss structures using the Jaya algorithm

Grzywiński Maksym

Computer Assisted Methods in Engineering and Science

|

2020

|

Vol. 27, no. 2-3

177--184

EN

The metaheuristic algorithm is proposed to solve the weight minimization problem of trussstructures, considering the shape and sizing design variables. Design variables are discreteand/or continuous. The design of truss structures is optimized by an efficient optimiza-tion algorithm called Jaya. The main feature of Jaya is that it does not require settingalgorithm-specific parameters. The algorithm has a very simple formulation in which thebasic idea is to approach the best solution and escape from the worst solution [6]. Analysesof structures are performed by a finite element code in MATLAB. The effectiveness of theJaya algorithm is demonstrated using two benchmark examples: planar truss 18-bar andspatial truss 39-bar, and compared with results in references.

3

Jaya Spider Monkey Optimization-driven Deep Convolutional LSTM for the prediction of COVID’19

Chander Satish, Padmanabha Vijaya, Mani Joseph

Bio-Algorithms and Med-Systems

|

2020

|

Vol. 16, no. 4

art. no. 20200030

EN

COVID’19 is an emerging disease and the precise epidemiological profile does not exist in the world. Hence, the COVID’19 outbreak is treated as a Public Health Emergency of the International Concern by the World Health Organization (WHO). Hence, an effective and optimal prediction of COVID’19 mechanism, named Jaya Spider Monkey Optimization-based Deep Convolutional long short-term classifier (JayaSMO-based Deep ConvLSTM) is proposed in this research to predict the rate of confirmed, death, and recovered cases from the time series data. The proposed COVID’19 prediction method uses the COVID’19 data, which is the trending domain of research at the current era of fighting the COVID’19 attacks thereby, to reduce the death toll. However, the proposed JayaSMO algorithm is designed by integrating the Spider Monkey Optimization (SMO) with the Jaya algorithm, respectively. The Deep ConvLSTM classifier facilitates to predict the COVID’19 from the time series data based on the fitness function. Besides, the technical indicators, such as Relative Strength Index (RSI), Rate of Change (ROCR), Exponential Moving Average (EMA), Williams %R, Double Exponential Moving Average (DEMA), and Stochastic %K, are extracted effectively for further processing. Thus, the resulted output of the proposed JayaSMO-based Deep ConvLSTM is employed for COVID’19 prediction. Moreover, the developed model obtained the better performance using the metrics, like Mean Square Error (MSE), and Root Mean Square Error (RMSE) by considering confirmed, death, and the recovered cases of COVID’19 for China and Oman. Thus, the proposed JayaSMO-based Deep ConvLSTM showed improved results with a minimal MSE of 1.791, and the minimal RMSE of 1.338 based on confirmed cases in Oman. In addition, the developed model achieved the death cases with the values of 1.609, and 1.268 for MSE and RMSE, whereas the MSE and the RMSE value of 1.945, and 1.394 is achieved by the developed model using recovered cases in China.

4

Optimization of post-tensioning forces in stay-cables of cable-stayed bridges

Atmaca Barbaros, Grzywiński Maksym, Dede Tayfun

Budownictwo o Zoptymalizowanym Potencjale Energetycznym

|

2019

|

Vol. 8 Nr 2

69--76

EN

Stay-cables are one of the most crucial structural elements of cable-stayed bridges. This structural element is used in the support of the bridge deck, transferring dead and live load exposed to the deck through the pylon and controls the vertical deck and horizontal pylon displacement with the help of post-tensioning forces of the stay-cables. Under the dead load of the structural and non-structural elements of the bridge, the vertical deck and horizontal pylon displacement must be almost zero. To determine the post-tensioning forces of the stay-cables, to ensure the desired displacement of deck and pylon with a trial-and-error procedure, is sometimes impossible. In this paper, we will determine the post-tensioning forces of a cable-stayed bridge’s stay-cable by developing a program that integrates a finite element analysis, and a Jaya algorithm with MATLAB codes. To achieve this aim an existing bridge was selected as an example. A threedimensional (3D) finite element model (FEM) of the selected bridge was created by SAP2000. 3D FEM of the selected bridge was repeatedly analyzed by using the Open Applicable Programming Interface (OAPI) properties of SAP2000. The results of numerical examples are presented and discussed to show efficiency of the optimization process. By minimizing the weight of the steel structure, CO2 emisions are also kept low.

PL

Kable stężające są jednym z najważniejszych elementów konstrukcyjnych mostów podwieszanych. Ten element konstrukcyjny jest wykorzystywany do podparcia płyty pomostu, przenosząc obciążenie stałe i zmienne z płyty na pylon i wpływa na przemieszczenie pionowe płyty i poziome pylonu za pomocą sił naciągających w kablach. Pod obciążeniem stałym i zmiennym elementów konstrukcyjnych mostu przemieszczenie pionowe pomostu i poziome pylonu musi wynosić prawie zero. W celu określenia sił sprężających w kablach, aby zapewnić pożądane przemieszczenie pomostu i pylonu, zastosowano metodę prób i błędów. W artykule określono siły sprężające cięgna mostu wantowego, opracowując program, który integruje analizę metody elementów skończonych (MES) oraz algorytm Jaya zakodowany w MATLAB-ie. Aby osiągnąć ten cel, jako przykład wybrano istniejący most. Trójwymiarowy model (3D) metody elementów skończonych (MES) wybranego mostu został stworzony w programie SAP2000. 3D MES wybranego mostu był wielokrotnie analizowany z użyciem właściwości Open Applicable Programming Interface (OAPI) SAP2000. Wyniki przykładów numerycznych zaprezentowano i omówiono w celu wykazania wydajności procesu optymalizacji. Minimalizując ciężar konstrukcji stalowej, emisje CO2 są również utrzymywane na niskim poziomie.

5

Abnormal Breast Detection in Mammogram Images by Feed-forward Neural Network Trained by Jaya Algorithm

Wang S., Rao R. V., Chen P., Zhang Y., Liu A., Wei L.

Fundamenta Informaticae

|

2017

|

Vol. 151, nr 1/4

191--211

EN

(Aim) Abnormal breast can be diagnosed using the digital mammography. Traditional manual interpretation method cannot yield high accuracy. (Method) In this study, we proposed a novel computer-aided diagnosis system for detecting abnormal breasts in mammogram images. First, we segmented the region-of-interest. Next, the weighted-type fractional Fourier transform (WFRFT) was employed to obtain the unified time-frequency spectrum. Third, principal component analysis (PCA) was introduced and used to reduce the spectrum to only 18 principal components. Fourth, feed-forward neural network (FNN) was utilized to generate the classifier. Finally, a novel algorithm-specific parameter free approach, Jaya, was employed to train the classifier. (Results) Our proposed WFRFT + PCA + Jaya-FNN achieved sensitivity of 92.26% ± 3.44%, specificity of 92.28% ± 3.58%, and accuracy of 92.27% ± 3.49%. (Conclusions) The proposed CAD system is effective in detecting abnormal breasts and performs better than 5 state-of-the-art systems. Besides, Jaya is more effective in training FNN than BP, MBP, GA, SA, and PSO.