A New Hybrid Optimization Technique for Scheduling of Periodic and Non-periodic Tasks

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Harendra Kumar ◽  
Isha Tyagi
Keyword(s):  
Optimization Technique ◽  
Hybrid Optimization ◽  
Periodic Tasks

scholarly journals A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
C. Mallika ◽  
S. Selvamuthukumaran
Keyword(s):  
Predictive Accuracy ◽  
Optimization Technique ◽  
Hybrid Optimization ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Diagnostic Model ◽  
Full Potential ◽  
Diabetes Diagnosis ◽  
Grey Wolf ◽  
Diagnosis System

AbstractDiabetes is an extremely serious hazard to global health and its incidence is increasing vividly. In this paper, we develop an effective system to diagnose diabetes disease using a hybrid optimization-based Support Vector Machine (SVM).The proposed hybrid optimization technique integrates a Crow Search algorithm (CSA) and Binary Grey Wolf Optimizer (BGWO) for exploiting the full potential of SVM in the diabetes diagnosis system. The effectiveness of our proposed hybrid optimization-based SVM (hereafter called CS-BGWO-SVM) approach is carefully studied on the real-world databases such as UCIPima Indian standard dataset and the diabetes type dataset from the Data World repository. To evaluate the CS-BGWO-SVM technique, its performance is related to several state-of-the-arts approaches using SVM with respect to predictive accuracy, Intersection Over-Union (IoU), specificity, sensitivity, and the area under receiver operator characteristic curve (AUC). The outcomes of empirical analysis illustrate that CS-BGWO-SVM can be considered as a more efficient approach with outstanding classification accuracy. Furthermore, we perform the Wilcoxon statistical test to decide whether the proposed cohesive CS-BGWO-SVM approach offers a substantial enhancement in terms of performance measures or not. Consequently, we can conclude that CS-BGWO-SVM is the better diabetes diagnostic model as compared to modern diagnosis methods previously reported in the literature.

A Comparison of Two Solution Techniques for the Inverse Problem of Simultaneously Estimating the Spatial Variations of Diffusion Coefficients and Source Terms

2003 ◽  
Author(s):  
Marcelo J. Colac¸o ◽  
Helcio R. B. Orlande ◽  
George S. Dulikravich ◽  
Fabio A. Rodrigues
Keyword(s):  
Inverse Problem ◽  
Gradient Method ◽  
Optimization Technique ◽  
Adjoint Problem ◽  
Diffusion Problem ◽  
Hybrid Optimization ◽  
Simultaneous Estimation ◽  
Random Errors ◽  
Flow Detection ◽  
Solution Techniques

This work deals with the simultaneous estimation of the spatially varying diffusion coefficient and of the source term distribution in a one-dimensional nonlinear diffusion problem. This work can be physically associated with the detection of material non-homogeneities such as inclusions, obstacles or cracks, heat conduction, groundwater flow detection, and tomography. Two solution techniques are applied in this paper to the inverse problem under consideration, namely: the conjugate gradient method with adjoint problem and a hybrid optimization algorithm. The hybrid optimization technique incorporates several of the most popular optimization modules; the Davidon-Fletcher-Powell (DFP) gradient method, a genetic algorithm (GA), the Nelder-Mead (NM) simplex method, quasi-Newton algorithm of Pshenichny-Danilin (LM), differential evolution (DE), and sequential quadratic programming (SQP). The accuracy of the two solution approaches was examined by using simulated transient measurements containing random errors in the inverse analysis.

Hybrid Tabu Search Hopfield Recurrent ANN Fuzzy Technique to the Production Planning Problems

2012 ◽  
Vol 2 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Pandian M. Vasant ◽  
Timothy Ganesan ◽  
Irraivan Elamvazuthi
Keyword(s):  
Tabu Search ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Oil Refinery ◽  
Hybrid Optimization ◽  
Real World Problem ◽  
Prime Objective ◽  
The Moment ◽  
Hybrid Optimization Techniques ◽  
Planning Problems

The fuzzy technology reveals that everything is a matter of degree. At the moment, many industrial production problems are solved by operational research optimization techniques, under the considerations of some real assumptions. In this paper, the authors have several applications of fuzzy linear, non-linear, non-continues and other mathematical programming applications. The prime objective of this paper is to investigate a new application to the literature and to solve the crude oil refinery production problem by using the hybrid optimization techniques of Tabu Search (TS), Hopfield Recurrent Artificial Neural Network (HRANN) and fuzzy approaches. In application, the real world problem of refinery model has been developed and thorough comparative studies have been carried on varies optimization techniques. The final results and findings reveal that, the hybrid optimization technique provides better, robust, efficient, flexible and stable solutions.

Study of Hybrid Optimization Technique for Grain Optimum Design

2017 ◽  
Vol 18 (4) ◽  
pp. 780-787 ◽  
Author(s):  
Seok-Hwan Oh ◽  
Yong-Chan Kim ◽  
Seung-Won Cha ◽  
Tae-Seong Roh
Keyword(s):  
Optimum Design ◽  
Optimization Technique ◽  
Hybrid Optimization

scholarly journals Hybrid Optimization Approach for the Design of Mechanisms Using a New Error Estimator

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
A. Sedano ◽  
R. Sancibrian ◽  
A. de Juan ◽  
F. Viadero ◽  
F. Egaña
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Technique ◽  
Hybrid Optimization ◽  
Error Estimator ◽  
Optimization Approach ◽  
Deterministic Approach ◽  
Dimensional Synthesis ◽  
Fitness Evaluation ◽  
Variable Space ◽  
Two Stages

A hybrid optimization approach for the design of linkages is presented. The method is applied to the dimensional synthesis of mechanism and combines the merits of both stochastic and deterministic optimization. The stochastic optimization approach is based on a real-valued evolutionary algorithm (EA) and is used for extensive exploration of the design variable space when searching for the best linkage. The deterministic approach uses a local optimization technique to improve the efficiency by reducing the high CPU time that EA techniques require in this kind of applications. To that end, the deterministic approach is implemented in the evolutionary algorithm in two stages. The first stage is the fitness evaluation where the deterministic approach is used to obtain an effective new error estimator. In the second stage the deterministic approach refines the solution provided by the evolutionary part of the algorithm. The new error estimator enables the evaluation of the different individuals in each generation, avoiding the removal of well-adapted linkages that other methods would not detect. The efficiency, robustness, and accuracy of the proposed method are tested for the design of a mechanism in two examples.

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