AN AIDED GENETIC ALGORITHM FOR MULTIPROCESSOR SCHEDULING

1999 ◽  
Vol 09 (03) ◽  
pp. 423-436 ◽  
Author(s):  
V. VAIDEHI ◽  
C. N. KRISHNAN ◽  
P. SWAMINATHAN
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Multiprocessor Scheduling ◽  
Initial Population ◽  
Task Graph ◽  
Filtering Algorithm ◽  
Schedule Length ◽  
Local Extrema ◽  
Conventional Genetic Algorithm ◽  
Random Nature

Genetic algorithms have been used for solving the problem of scheduling the tasks represented by a task graph onto parallel computing architectures to minimize the schedule length of the task graph. Due to the random nature of the initial population they however face the local extrema problem which could make the resulting schedules sub-optimal. To minimize this problem, an Aided Genetic Algorithm(AGA) is proposed in this paper, in which a member of the initial population of the Genetic algorithm is obtained from a heuristic pre-scheduler. It is found that the AGA achieves the required convergence in (a) lesser number of iterations, and (b) lesser number of trials in obtaining the near-optimal solution compared to the conventional genetic algorithm. The proposed AGA also takes the inter-task communication into account while scheduling. The method is then applied to the problem of optimally scheduling the Kalman filtering algorithm onto a multi-transputer network. The results are experimentally on a network of T-805 transputers.

scholarly journals An Optimal Solution in cloud computing for Load Balancing using Genetic Algorithm

2019 ◽  
Vol 8 (6) ◽  
pp. 2490-2494
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Cloud Services ◽  
Research Trend ◽  
Schedule Length ◽  
Research Gap ◽  
Load Balancing Algorithm

Cloud computing is a research trend which bring various cloud services to the users. Cloud environment face various challenges and issues to provide efficient services. In this paper, a novel Genetic Algorithm based load balancing algorithm has been implemented to balance the load in the network. The literature review has been studied to understand the research gap. More specifically, load balancing technique authenticate the network by enabling Virtual Machines (VM). The proposed technique has been further evaluated using the Schedule Length Runtime (SLR) and Energy consumption (EC) parameters. Overall, the effective results has been obtained such as 46% improvement in consuming the energy and 12 % accuracy for the SLR measurement. In addition, results has been compared with the conventional approaches to validate the outcomes.

Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem

2013 ◽  
Vol 365-366 ◽  
pp. 194-198 ◽  
Author(s):  
Mei Ni Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Simulation Software ◽  
Initial Population ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Search Speed ◽  
Vehicle Path

mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

A Natural Evolution Based Numerical Optimisation Framework to Develop and Enhance Airfoil-Slat Arrangement

2019 ◽  
Author(s):  
Sushrut Kumar ◽  
Priyam Gupta ◽  
Raj Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Fuel Efficiency ◽  
Optimal Solution ◽  
Leading Edge ◽  
Initial Population ◽  
Invasive Weed ◽  
Fitness Value ◽  
Leading Edge Slats ◽  
Dynamics Simulations

Abstract Leading Edge Slats are popularly being put into practice due to their capability to provide a significant increase in the lift generated by the wing airfoil and decrease in the stall. Consequently, their optimum design is critical for increased fuel efficiency and minimized environmental impact. This paper attempts to develop and optimize the Leading-Edge Slat geometry and its orientation with respect to airfoil using Genetic Algorithm. The class of Genetic Algorithm implemented was Invasive Weed Optimization as it showed significant potential in converging design to an optimal solution. For the study, Clark Y was taken as test airfoil. Slats being aerodynamic devices require smooth contoured surfaces without any sharp deformities and accordingly Bézier airfoil parameterization method was used. The design process was initiated by producing an initial population of various profiles (chromosomes). These chromosomes are composed of genes which define and control the shape and orientation of the slat. Control points, Airfoil-Slat offset and relative chord angle were taken as genes for the framework and different profiles were acquired by randomly modifying the genes within a decided design space. To compare individual chromosomes and to evaluate their feasibility, the fitness function was determined using Computational Fluid Dynamics simulations conducted on OpenFOAM. The lift force at a constant angle of attack (AOA) was taken as fitness value. It was assigned to each chromosome and the process was then repeated in a loop for different profiles and the fittest wing slat arrangement was obtained which had an increase in CL by 78% and the stall angle improved to 22°. The framework was found capable of optimizing multi-element airfoil arrangements.

scholarly journals Route Planning Based on Genetic Algorithm

2018 ◽  
Vol 10 (2) ◽  
pp. 122 ◽  
Author(s):  
Lin Li ◽  
Yuhua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Solution Method ◽  
Route Planning ◽  
Optimal Solution ◽  
Initial Population ◽  
Smoothing Algorithm ◽  
Aircraft Fuel ◽  
Consumption Model ◽  
Feasible Solutions

This paper mainly deals with the planning of aviation route and needs to determine the model to find out the shortest path. In this paper, we combine the methods of simulated annealing and genetic algorithm, and obtained the optimal solution method. Firstly, Genetic Algorithm (GA) uses the modified circle algorithm to find some feasible solutions to the approximate initial population, and then transforms them through simulated and crossover operations. This paper also introduces the aircraft fuel consumption model and the cubical smoothing algorithm with five-point approximation to reduce the aircraft fuel consumption and parts loss. The simulation results show that the accuracy of the route planning based on genetic algorithm is higher, while consumes less fuel and takes less sharp turns.

scholarly journals Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

2018 ◽  
Author(s):  
Morteza Madhkhan ◽  
Mohammad Reza Baradaran
Keyword(s):  
Genetic Algorithm ◽  
Rate Of Convergence ◽  
Optimal Solution ◽  
Search Space ◽  
Initial Population ◽  
Generation Process ◽  
Improved Genetic Algorithm ◽  
Cross Sectional ◽  
Design Constraints ◽  
Matlab Programming

Genetic Algorithm (GA) is one of the most widely used optimization algorithms. This algorithm consists of five stages, namely population generation, crossover, mutation, evaluation, and selection. This study presents a modified version of GA called Improved Genetic Algorithm (IGA) for the optimization of steel frame designs. In the IGA, the rate of convergence to the optimal solution is increased by splitting the population generation process to two stages. In the first stage, the initial population is generated by random selection of members from among AISC W-shapes. The generated population is then evaluated in another stage, where the member that does not satisfy the design constraints are replaced with stronger members with larger cross sectional area. This process continues until all design constraints are satisfied. Through this process, the initial population will be improved intelligently so that the design constraints fall within the allowed range. For performance evaluation and comparison, the method was used to design and optimize 10-story and 24-story frames based on the LRFD method as per AISC regulations with the finite element method used for frame analysis. Structural analysis, design, and optimization were performed using a program written with MATLAB programming language. The results show that using the proposed method (IGA) for frame optimization reduces the volume of computations and increases the rate of convergence, thus allowing access to frame designs with near-optimal weights in only a few iterations. Using the IGA also limits the search space to the area of acceptable solutions.

Scheduling for the Flexible Job-Shop Problem Based on Genetic Algorithm (GA)

2012 ◽  
Vol 457-458 ◽  
pp. 616-619
Author(s):  
Shun Cheng Fan ◽  
Jin Feng Wang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Solution ◽  
Initial Population ◽  
Flexible Job Shop ◽  
Chromosome Representation ◽  
Local Optimal Solution ◽  
Crossover And Mutation ◽  
Flexible Job Shop Problem

In this paper, we analyze the characteristics of the flexible job-shop scheduling problem(FJSP). A novel genetic algorithm is elaborated to solve the FJSP. An improved chromosome representation is used to conveniently represent a solution of the FJSP. Initial population is generated randomly. The relevant selection, crossover and mutation operation is also designed. It jumped from the local optimal solution, and the search area of solution is improved. Finally, the algorithm is tested on instances of 4 jobs and 6 machines. Computational results prove the proposed genetic algorithm effective for solving the FJSP.

GENETIC ALGORITHM WITH ELITIST MODEL AND ITS CONVERGENCE

1996 ◽  
Vol 10 (06) ◽  
pp. 731-747 ◽  
Author(s):  
DINABANDHU BHANDARI ◽  
C. A. MURTHY ◽  
SANKAR K. PAL
Keyword(s):  
Genetic Algorithm ◽  
Markov Chain ◽  
Optimal Solution ◽  
Initial Population ◽  
Global Optimal Solution ◽  
Mutation Operation ◽  
Finite State ◽  
Global Optimal

In this article, the genetic algorithm with elitist model (EGA) is modeled as a finite state Markov chain. A state in the Markov chain denotes a population together with a potential string. Proof for the convergence of an EGA to the best chromosome (string), among all possible chromosomes, is provided here. Mutation operation has been found to be essential for convergence. It has been shown that an EGA converges to the global optimal solution with any choice of initial population.

scholarly journals A New Optimization Approach for Maximizing the Photovoltaic Panel Power Based on Genetic Algorithm and Lagrange Multiplier Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Mahdi M. M. El-Arini ◽  
Ahmed M. Othman ◽  
Ahmed Fathy
Keyword(s):  
Genetic Algorithm ◽  
Output Power ◽  
Lagrange Multiplier ◽  
Electric Energy ◽  
Optimal Solution ◽  
Maximum Efficiency ◽  
Initial Population ◽  
Optimization Approach ◽  
Optimal Point ◽  
Pv Panel

In recent years, the solar energy has become one of the most important alternative sources of electric energy, so it is important to operate photovoltaic (PV) panel at the optimal point to obtain the possible maximum efficiency. This paper presents a new optimization approach to maximize the electrical power of a PV panel. The technique which is based on objective function represents the output power of the PV panel and constraints, equality and inequality. First the dummy variables that have effect on the output power are classified into two categories: dependent and independent. The proposed approach is a multistage one as the genetic algorithm, GA, is used to obtain the best initial population at optimal solution and this initial population is fed to Lagrange multiplier algorithm (LM), then a comparison between the two algorithms, GA and LM, is performed. The proposed technique is applied to solar radiation measured at Helwan city at latitude 29.87°, Egypt. The results showed that the proposed technique is applicable.

scholarly journals Genetic Algorithm Based Timetabling Program

2020 ◽  
Vol 2 (2) ◽  
pp. 20-31
Author(s):  
Mutlu YAPICI ◽  
Ömer Faruk BAY
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Initial Population ◽  
Test Results ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Resource Limitations ◽  
Real World Problem ◽  
Solution Methods ◽  
Course Timetabling Problem

Course Timetabling Problem is concerned with assigning a number of courses and instructors to classrooms by taking the constraints into consideration. Generally, this problem is typically resolved manually; and due to the large variety of constraints, resource limitations and complicated human factors involved, it takes a lot of time and manpower. It is considered as one of the most time-consuming problems faced by universities and colleges today. In this study, we aimed to develop a genetic algorithm-based timetabling software to bring a solution to course timetabling problem, which is a real world problem. This software allows constraints to be entered easily and allows that optimal solutions are found. To find the most suitable solution for optimization, two different solution methods, a full-genetic algorithm and a partial-genetic algorithm, were tested. Test results showed that when we start the full genetic algorithms from randomly generated initial population, it takes quite some time to obtain the appropriate solution. With the partial-genetic algorithm, an optimal solution was achieved much more quickly than the full genetic algorithm.

An Improved Genetic Algorithm on Hybrid Information Scheduling

2019 ◽  
Vol 13 (4) ◽  
pp. 416-423 ◽  
Author(s):  
Jingmei Li ◽  
Qiao Tian ◽  
Fangyuan Zheng ◽  
Weifei Wu
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Selection Strategy ◽  
Initial Population ◽  
List Scheduling ◽  
Solution Quality ◽  
Fitness Value ◽  
Information Scheduling

Background: Patents suggest that efficient hybrid information scheduling algorithm is critical to achieve high performance for heterogeneous multi-core processors. Because the commonly used list scheduling algorithm obtains the approximate optimal solution, and the genetic algorithm is easy to converge to the local optimal solution and the convergence rate is slow. Methods: To solve the above two problems, the thesis proposes a hybrid algorithm integrating list scheduling and genetic algorithm. Firstly, in the task priority calculation phase of the list scheduling algorithm, the total cost of the current task node to the exit node and the differences of its execution cost on different processor cores are taken into account when constructing the task scheduling list, then the task insertion method is used in the task allocation phase, thus obtaining a better scheduling sequence. Secondly, the pre-acquired scheduling sequence is added to the initial population of the genetic algorithm, and then a dynamic selection strategy based on fitness value is adopted in the phase of evolution. Finally, the cross and mutation probability in the genetic algorithm is improved to avoid premature phenomenon. Results: With a series of simulation experiments, the proposed algorithm is proved to have a faster convergence rate and a higher optimal solution quality. Conclusion: The experimental results show that the ICLGA has the highest quality of the optimal solution than CPOP and GA, and the convergence rate of ICLGA is faster than that of GA.

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