Genetic algorithm to speed up modeling turn-around-time

2021 ◽  
Author(s):  
Jiao Huang ◽  
Yongfa Fan ◽  
Bin Cheng ◽  
Yunbo Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Speed Up ◽  
Turn Around Time

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2015 ◽  
pp. 334-347 ◽  
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

scholarly journals Genetic Algorithm for Optimizing Routing Design and Fleet Allocation of Freeway Service Overlapping Patrol

2018 ◽  
Vol 10 (11) ◽  
pp. 4120 ◽  
Author(s):  
Xiuqiao Sun ◽  
Jian Wang ◽  
Weitiao Wu ◽  
Wenjia Liu
Keyword(s):  
Genetic Algorithm ◽  
Population Diversity ◽  
Decision Makers ◽  
Incident Response ◽  
Matrix Encoding ◽  
Routing Design ◽  
Speed Up ◽  
Fleet Allocation ◽  
Patrol Routing ◽  
Elitist Strategy

The freeway service patrol problem involves patrol routing design and fleet allocation on freeways that would help transportation agency decision-makers when developing a freeway service patrols program and/or altering existing route coverage and fleet allocation. Based on the actual patrol process, our model presents an overlapping patrol model and addresses patrol routing design and fleet allocation in a single integrated model. The objective is to minimize the overall average incident response time. Two strategies—overlapping patrol and non-overlapping patrol—are compared in our paper. Matrix encoding is applied in the genetic algorithm (GA), and to maintain population diversity and avoid premature convergence, a niche strategy is incorporated into the traditional genetic algorithm. Meanwhile, an elitist strategy is employed to speed up the convergence. Using numerical experiments conducted based on data from the Sioux Falls network, we clearly show that: overlapping patrol strategy is superior to non-overlapping patrol strategy; the GA outperforms the simulated annealing (SA) algorithm; and the computational efficiency can be improved when LINGO software is used to solve the problem of fleet allocation.

scholarly journals An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Yunqing Rao ◽  
Dezhong Qi ◽  
Jinling Li
Keyword(s):  
Genetic Algorithm ◽  
Parallel Machines ◽  
Convergence Rates ◽  
Cutting Stock ◽  
Hierarchical Coding ◽  
Crossover Probability ◽  
Speed Up ◽  
Adaptive Crossover ◽  
Process Constraints ◽  
Hierarchical Genetic Algorithm

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involvesncutting patterns formnon-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

An Improved Hybrid Genetic Algorithm and Performance Study

2012 ◽  
Vol 482-484 ◽  
pp. 95-98
Author(s):  
Wei Dong Ji ◽  
Ke Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Convergence Speed ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Performance Study ◽  
Speed Up ◽  
And Performance

Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

Master-Slave Parallel Genetic Algorithm Based on MapReduce Using Cloud Computing

2011 ◽  
Vol 121-126 ◽  
pp. 4023-4027 ◽  
Author(s):  
Guang Ming Li ◽  
Wen Hua Zeng ◽  
Jian Feng Zhao ◽  
Min Liu
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
High Performance ◽  
Low Cost ◽  
Parallel Genetic Algorithm ◽  
Parallel Genetic Algorithms ◽  
Computing Platform ◽  
Speed Up ◽  
Cloud Computing Platform ◽  
High Performance Computer

The implementation platforms of parallel genetic algorithms (PGAs) include high performance computer, cluster and Grid. Contrast with the traditional platform, a Master-slave PGA based on MapReduce (MMRPGA) of cloud computing platform was proposed. Cloud computing is a new computer platform, suites for larger-scale computing and is low cost. At first, describes the design of MMRPGA, in which the whole evolution is controlled by Master and the fitness computing is assigned to Slaves; then deduces the theoretical speed-up of MMRPGA; at last, implements MMRPGA on Hadoop and compares the speed-up with traditional genetic algorithm, the experiment result shows MMRPGA can achieve slightly lower linear speed-up with Mapper’s number.

Research and Implementation of Professional Quality of Personnel Evaluation System Based on Multi-Dimensional

2014 ◽  
Vol 687-691 ◽  
pp. 4725-4729
Author(s):  
Min Jiang ◽  
Ying Jiang
Keyword(s):  
Genetic Algorithm ◽  
Evaluation System ◽  
Fitness Function ◽  
Paper Test ◽  
Simulation Experiments ◽  
Automatic Group ◽  
Paper Making ◽  
Professional Quality ◽  
Speed Up

this paper uses a new genetic algorithm (New Genetic Algorithm, NGA) to implement the automatic group volume function, solving the problem that the paper is not fully considered scores distribution of knowledge points in the test paper (test dimension) in the system using traditional genetic algorithm (Genetic Algorithm, GA) to implement automatic group volume, putting forward and redefining the fitness function to speed up the convergence. Simulation experiments show that the NGA algorithm is not only efficient, but can generate a valid paper, making the paper score can on the multi-dimensional of the test as far as possible to achieve uniform distribution.

scholarly journals Modified Grasshopper Optimization Algorithm Based Genetic Algorithm for Global Optimization Problems: The System of Nonlinear Equations Case Study

2021 ◽  
Author(s):  
Hala A. Omar ◽  
Mohammed El-Shorbagy
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Local Minimum ◽  
Optimization Problems ◽  
Search Space ◽  
System Of Nonlinear Equations ◽  
Grasshopper Optimization Algorithm ◽  
Speed Up ◽  
Grasshopper Optimization

Abstract Grasshopper optimization algorithm (GOA) is one of the promising optimization algorithms for optimization problems. But, it has the main drawback of trapping into a local minimum, which causes slow convergence or inability to detect a solution. Several modifications and combinations have been proposed to overcome this problem. In this paper, a modified grasshopper optimization algorithm (MGOA) based genetic algorithm (GA) is proposed to overcome this problem. Modifications rely on certain mathematical assumptions and varying the domain of the Cmax control parameter to escape from the local minimum and move the search process to a new improved point. Parameter C is one of the most important parameters in GOA where it balances the exploration and exploitation of the search space. These modifications aim to lead to speed up the convergence rate by reducing the repeated solutions and the number of iterations. The proposed algorithm will be tested on the 19 main test functions to verify and investigate the influence of the proposed modifications. In addition, the algorithm will be applied to solve 5 different cases of nonlinear systems with different types of dimensions and regularity to show the reliability and efficiency of the proposed algorithm. Good results were achieved compared to the original GOA.

scholarly journals Construct Linear Polynomial Complementary Transformation for NP-Completeness Using Parallel Genetic Algorithm

2016 ◽  
Author(s):  
Tarik Eltaeib ◽  
Julius Dichter
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Genetic Algorithm ◽  
Algorithm Optimization ◽  
Parallel Genetic Algorithms ◽  
Np Completeness ◽  
Linear Polynomial ◽  
Optimization Function ◽  
Speed Up ◽  
Complementary Equation

This paper examines the correlation between numbers of computer cores in parallel genetic algorithms. The objective to determine the linear polynomial complementary equation in order represent the relation between number of parallel processing and optimum solutions. Model this relation as optimization function (f(x)) which able to produce many simulation results. F(x) performance is outperform genetic algorithms. Compression results between genetic algorithm and optimization function is done. Also the optimization function give model to speed up genetic algorithm. Optimization function is a complementary transformation which maps a TSP given to linear without changing the roots of the polynomials.

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