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jih-1817
Local Search Algorithms for Multi-criteria Single Machine Scheduling Problem
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   Real life scheduling problems require the decision maker to consider a number of criteria before arriving at any decision. In this paper, we consider the multi-criteria scheduling problem of n jobs on single machine to minimize a function of five criteria denoted by total completion times (∑), total tardiness (∑), total earliness (∑), maximum tardiness () and maximum earliness (). The single machine total tardiness problem and total earliness problem are already NP-hard, so the considered problem is strongly NP-hard.

We apply two local search algorithms (LSAs) descent method (DM) and simulated annealing method (SM) for the 1// (∑∑∑) problem (SP) to find near optimal solutions. The local search methods are used to speed up the process of finding a good enough solution, where an exhaustive search is impractical for the exact solution. The two heuristic (DM and SM) were compared with the branch and bound (BAB) algorithm in order to evaluate effectiveness of the solution methods.

            Some experimental results are presented to show the applicability of the (BAB) algorithm and (LSAs). With a reasonable time, (LSAs) may solve the problem (SP) up to 5000 jobs.

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Publication Date
Sun Dec 01 2019
Journal Name
Applied Soft Computing
A new evolutionary multi-objective community mining algorithm for signed networks
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Publication Date
Tue Mar 30 2021
Journal Name
Baghdad Science Journal
Solvability of Some Types for Multi-fractional Integro-Partial Differential Equation
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In this article, the solvability of some proposal types of the multi-fractional integro-partial differential system has been discussed in details by using the concept of abstract Cauchy problem and certain semigroup operators and some necessary and sufficient conditions. 

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Publication Date
Tue Oct 16 2018
Journal Name
Springer Science And Business Media Llc
MOGSABAT: a metaheuristic hybrid algorithm for solving multi-objective optimisation problems
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Publication Date
Wed Jun 01 2022
Journal Name
Journal Of King Saud University - Computer And Information Sciences
Heuristic initialization of PSO task scheduling algorithm in cloud computing
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Publication Date
Sun Jan 20 2019
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Text Classification Based on Weighted Extreme Learning Machine
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The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed   a great competence of the proposed WELM compared to the ELM. 

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Publication Date
Tue Jan 07 2025
Journal Name
Journal Of Physical Education
Criteria Some Power Defined By Speed Endurance In Youth Basketball
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Publication Date
Sun Apr 30 2017
Journal Name
Journal Of Engineering
Evaluation of Urban Planning Projects Criteria Using Fuzzy AHP Technique
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In this research,  Fuzzy Analytic Hierarchy Process technique is applied (Fuzzy AHP) which is one of  multi-criteria decision making techniques to evaluate the criteria for urban planning projects, the project of developing master plan of Al-Muqdadiyah city to 2035 has been chosen as a case study. The researcher prepared a list of criteria in addition to the authorized departments criteria and previous researches in order to choose optimized master plan according to these criteria. This research aims at employing the foundations of (Fuzzy AHP) technique in evaluating urban planning criteria precisely and flexible. The results of the data analysis to the individuals of the sample who are specialists, in this aspect.  The la

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Publication Date
Mon Mar 11 2019
Journal Name
Baghdad Science Journal
Developing Load Balancing for IoT - Cloud Computing Based on Advanced Firefly and Weighted Round Robin Algorithms
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The evolution of the Internet of things (IoT) led to connect billions of heterogeneous physical devices together to improve the quality of human life by collecting data from their environment. However, there is a need to store huge data in big storage and high computational capabilities.   Cloud computing can be used to store big data.  The data of IoT devices is transferred using two types of protocols: Message Queuing Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP). This paper aims to make a high performance and more reliable system through efficient use of resources. Thus, load balancing in cloud computing is used to dynamically distribute the workload across nodes to avoid overloading any individual r

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Publication Date
Sat Dec 01 2018
Journal Name
Al-khwarizmi Engineering Journal
Experimental and Numerical Investigation for Simulation of Thermophysical Properties for Polypropylene 575 Polymer Melts in Single Screw Extruder
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A numerical model for Polypropylene 575 polymer melts flow along the solid conveying screw of a single screw extruder under constant heat flux using ANSYS-FLUENT 17.2 software has been conducted. The model uses the thermophysical properties such as Viscosity, thermal conductivity, Specific heat and density of polypropylene 575 that measured as a function of temperature, and residence time data for process simulation. The numerical simulation using CFD models for single screw extruder and the polymer extrusion was analysed for parameters such as (thermal conductivity, specific heat, density and viscosity) reveals a high degree of similarity to experimental data measured. The most important outcome of this study is that geometrical, parame

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Publication Date
Wed Apr 15 2020
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Optimizing Linear Models via Sinusoidal Transformation for Boosted Machine Learning in Medicine: Sinusoidal Optimization of Linear Models
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Background: Machine learning relies on a hybrid of analytics, including regression analyses. There have been no attempts to deploy a sinusoidal transformation of data to enhance linear regression models.
Objectives:
We aim to optimize linear models by implementing sinusoidal transformation to minimize the sum of squared error.
Methods:
We implemented non-Bayesian statistics using SPSS and MatLab. We used Excel to generate 30 trials of linear regression models, and each has 1,000 observations. We utilized SPSS linear regression, Wilcoxon signed-rank test, and Cronbach’s alpha statistics to evaluate the performance of the optimization model. Results: The sinusoidal

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