Application of an Improved Immune Evolutionary Algorithm to Grid Task Scheduling

2011 ◽  
Vol 219-220 ◽  
pp. 1383-1388
Author(s):  
Sheng Jun Xue ◽  
Fang Fang Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Task Scheduling ◽  
Optimal Solution ◽  
Local Optimum ◽  
Adaptive Function ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
And Performance ◽  
Np Problem ◽  
Immune Evolutionary Algorithm

Grid task scheduling is an NP problem , performance of scheduling algorithms greatly influences scheduling results. Aiming at the shortages of the existing Evolutionary Algorithm, such as premature convergence, search process easily falling into local optimum, poor scheduling results and so on, this paper proposed an improved immune Evolutionary Algorithm which introduced concentration mechanism in the immune system into Immune Evolutionary Algorithm and adjusted regulator to adaptive function. Simulation experiment shows that, convergence speed and performance of the improved algorithm are significantly improved and it can better converge to global optimal solution, applying the algorithm to grid task scheduling can obtain better scheduling results.

Bee Immune Evolutionary Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 1184-1187 ◽  
Author(s):  
Zuo Yong Li ◽  
Chun Xue Yu ◽  
Lei Zang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Evolutionary Process ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Second Best ◽  
Evolutionary Genetic ◽  
Global Optimal ◽  
Immune Evolutionary Algorithm

The bee immune evolutionary algorithm was proposed in order to improve effectively the optimal ability of bee evolutionary genetic algorithm. In the evolutionary process of bee, the algorithm made on immune evolutionary iteration calculation, generate next-generation population, in the proportions of fitness values for the best individual and second-best individuals in each generation. Because the algorithm takes in the neighborhood of space search as well out the neighborhood of space search for the some optimal individuals, meanwhile, with iterative numbers increase, capability of local search can be strengthened gradually; the bee immune evolutionary algorithm can approach the global optimal solution with higher accuracy. The calculated results for typical best functions show that the bee immune evolutionary algorithm has better optimal capability and stability.

Improved Method of Hybrid Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4014-4017
Author(s):  
Lei Ding ◽  
Yong Jun Luo ◽  
Yang Yang Wang ◽  
Zheng Li ◽  
Bing Yin Yao
Keyword(s):  
Genetic Algorithm ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Local Optimum ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Better Than

On account of low convergence of the traditional genetic algorithm in the late,a hybrid genetic algorithm based on conjugate gradient method and genetic algorithm is proposed.This hybrid algorithm takes advantage of Conjugate Gradient’s certainty, but also the use of genetic algorithms in order to avoid falling into local optimum, so it can quickly converge to the exact global optimal solution. Using Two test functions for testing, shows that performance of this hybrid genetic algorithm is better than single conjugate gradient method and genetic algorithm and have achieved good results.

Task Scheduling of Parallel Development Projects Using Genetic Algorithms

2004 ◽  
Author(s):  
Miao Zhuang ◽  
Ali A. Yassine
Keyword(s):  
Genetic Algorithm ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Local Optimum ◽  
Development Projects ◽  
Scheduling Problems ◽  
Global Optimal Solution ◽  
Genetic Algorithm Approach ◽  
Project Scheduling Problem ◽  
Global Optimal

Resources for development projects are often scarce in the real world. Generally, many projects are to be completed that rely on a common pool of resources. Besides resource constraints, there exists data dependency among tasks within each project. A genetic algorithm approach with one-point uniform crossover and a refresh operator is proposed to minimize the overall duration or makespan of multiple projects in a resource constrained multi project scheduling problem (RCMPSP) without violating inter-project resource constraints or intra-project precedence constraints. The proposed GA incorporates stochastic feedback or rework of tasks. It has the capability of capturing the local optimum for each generation and therefore ensuring a global best solution. The proposed Genetic Algorithm, with several variants of GA parameters is tested on sample scheduling problems with and without stochastic feedback. This algorithm demonstrates to provide a quick convergence to a global optimal solution and detect the most likely makespan range for parallel projects of tasks with stochastic feedback.

Task Scheduling in Cloud Computing with Improved Firefly Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 3189-3193 ◽  
Author(s):  
Li Feng Zhao ◽  
Sheng Han Zhou ◽  
Wen Bing Chang
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Experimental Analysis ◽  
Firefly Algorithm ◽  
Objective Function Value ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Computing Model ◽  
Improved Firefly Algorithm ◽  
Global Optimal

The paper improved a task scheduling in cloud computing based on intelligence firefly algorithm. Firstly, the study finds the better solution of cloud computing task scheduling with intelligence firefly algorithm. Then the better solution was turned into the improved firefly algorithm to find the global optimal solution through improved firefly information communications. The Experimental Analysis result suggest the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution time of objective function value is shorten.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

scholarly journals Best Proximity Point Results in Complex Valued Metric Spaces

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Binayak S. Choudhury ◽  
Nikhilesh Metiya ◽  
Pranati Maity
Keyword(s):  
Minimum Distance ◽  
Metric Spaces ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Complex Valued Metric Space ◽  
Fixed Point Equation ◽  
Point Equation ◽  
Global Optimal ◽  
Metric Function ◽  
Complex Valued

We introduce the concept of proximity points for nonself-mappings between two subsets of a complex valued metric space which is a recently introduced extension of metric spaces obtained by allowing the metric function to assume values from the field of complex numbers. We apply this concept to obtain the minimum distance between two subsets of the complex valued metric spaces. We treat the problem as that of finding the global optimal solution of a fixed point equation although the exact solution does not in general exist. We also define and use the concept of P-property in such spaces. Our results are illustrated with examples.

scholarly journals Prototype Filter Design Based on Channel Estimation for FBMC/OQAM Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yongjin Liu ◽  
Xihong Chen ◽  
Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Channel Estimation ◽  
Optimization Problem ◽  
Filter Design ◽  
Stop Band ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Prototype Filter ◽  
Global Optimal

A prototype filter design for FBMC/OQAM systems is proposed in this study. The influence of both the channel estimation and the stop-band energy is taken into account in this method. An efficient preamble structure is proposed to improve the performance of channel estimation and save the frequency spectral efficiency. The reciprocal of the signal-to-interference plus noise ratio (RSINR) is derived to measure the influence of the prototype filter on channel estimation. After that, the process of prototype filter design is formulated as an optimization problem with constraint on the RSINR. To accelerate the convergence and obtain global optimal solution, an improved genetic algorithm is proposed. Especially, the History Network and pruning operator are adopted in this improved genetic algorithm. Simulation results demonstrate the validity and efficiency of the prototype filter designed in this study.

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