A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in Computational Grid

2018 ◽  
Vol 9 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das
Keyword(s):  
Differential Evolution ◽  
Firefly Algorithm ◽  
Job Scheduling ◽  
Population Based ◽  
Computational Grids ◽  
Hybrid Population ◽  
Shared Resources ◽  
De Algorithm ◽  
Processing Cost ◽  
Comparison Results

Scheduling jobs in computational Grids is considered as NP-complete problem owing to the heterogeneity of shared resources. The resources belong to many distributed administrative domains that enforce various management policies. Therefore, the use of meta-heuristics are more appropriate option in obtaining optimal results. In this article, a novel hybrid population-based global optimization algorithm, called the Hybrid Firefly Algorithm and the Differential Evolution (HFA-DE), is proposed by combining the merits of both the Firefly Algorithm and Differential Evolution. The Firefly Algorithm and the Differential Evolution are executed in parallel to support information sharing amongst the population and thus enhance searching efficiency. The proposed HFA-DE algorithm reduces the schedule makespan, processing cost, and improves resource utilization. The HFA-DE is compared with the standard Firefly Algorithm, the Differential Evolution and the Particle Swarm Optimization algorithms on all these parameters. The comparison results exhibit that the proposed algorithm outperforms the other three algorithms.

A new parameter setting-based modified differential evolution for function optimization

2020 ◽  
Vol 11 (04) ◽  
pp. 2050029
Author(s):  
Sukanta Nama ◽  
Apu Kumar Saha
Keyword(s):  
Performance Evaluation ◽  
Differential Evolution ◽  
Real World ◽  
Population Based ◽  
Function Optimization ◽  
Mutation Operator ◽  
Control Parameters ◽  
Test Results ◽  
Parameter Setting ◽  
De Algorithm

The population-based efficient iterative evolutionary algorithm (EA) is differential evolution (DE). It has fewer control parameters but is useful when dealing with complex problems of optimization in the real world. A great deal of progress has already been made and implemented in various fields of engineering and science. Nevertheless, DE is prone to the setting of control parameters in its performance evaluation. Therefore, the appropriate adjustment of the time-consuming control parameters is necessary to achieve optimal DE efficiency. This research proposes a new version of the DE algorithm control parameters and mutation operator. For the justifiability of the suggested method, several benchmark functions are taken from the literature. The test results are contrasted with other literary algorithms.

An Improved Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Local Optimum ◽  
Local Optima ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.

Cost Optimization Problem of Hybrid Flow-Shop Based on Differential Evolution Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 1692-1700
Author(s):  
Zhong Hua Han ◽  
Xiang Bin Meng ◽  
Bin Ma ◽  
Chang Tao Wang
Keyword(s):  
Differential Evolution ◽  
Production Cost ◽  
Job Scheduling ◽  
Flow Shop ◽  
Fitness Function ◽  
Cost Optimization ◽  
Differential Evolution Algorithm ◽  
Hybrid Flow Shop ◽  
Processing Cost ◽  
Evolution Algorithm

A differential evolution algorithm based job scheduling method is presented, whose optimization target is production cost. The cost optimization model of hybrid flow-shop is thereby constructed through considering production cost as a factor in scheduling problem of hybrid flow-shop. In the implementation process of the method, DE is used to take global optimization and find which machine the jobs should be assigned on at each stage, which is also called the process route of the job; then the local assignment rules are used to determine the job’s starting time and processing sequence at each stage. With converting time-based scheduling results to fitness function which is comprehensively considering the processing cost, waiting costs, and the products storage costs, the processing cost is taken as the optimization objective. The numerical results show the effectiveness of the algorithm after comparing between multi-group programs.

scholarly journals Data Clustering based on Modified Differential Evolution and Quasi-Opposition-based Learning

2020 ◽  
Vol 13 (6) ◽  
pp. 168-178
Author(s):  
Pyae Cho ◽  
◽  
Thi Nyunt ◽  
Keyword(s):  
Differential Evolution ◽  
Heuristic Search ◽  
Clustering Algorithm ◽  
Population Based ◽  
Initial Population ◽  
Local Optima ◽  
Opposition Based Learning ◽  
De Algorithm ◽  
Mutation Strategy

Differential Evolution (DE) has become an advanced, robust, and proficient alternative technique for clustering on account of their population-based stochastic and heuristic search manners. Balancing better the exploitation and exploration power of the DE algorithm is important because this ability influences the performance of the algorithm. Besides, keeping superior solutions for the initial population raises the probability of finding better solutions and the rate of convergence. In this paper, an enhanced DE algorithm is introduced for clustering to offer better cluster solutions with faster convergence. The proposed algorithm performs a modified mutation strategy to improve the DE’s search behavior and exploits Quasi-Opposition-based Learning (QBL) to choose fitter initial solutions. This mutation strategy that uses the best solution as a target solution and applies three differentials contributes to avoiding local optima trap and slow convergence. The QBL based initialization method also contributes to increasing the quality of the clustering results and convergence rate. The experimental analysis was conducted on seven real datasets from the UCI repository to evaluate the performance of the proposed clustering algorithm. The obtained results showed that the proposed algorithm achieves more compact clusters and stable solutions than the competing conventional DE variants. Moreover, the performance of the proposed algorithm was compared with the existing state of the art clustering techniques based on DE. The corresponding results also pointed out that the proposed algorithm is comparable to other DE based clustering approaches in terms of the value of the objective functions. Therefore, the proposed algorithm can be regarded as an efficient clustering tool.

scholarly journals Differential evolution algorithm of soft island model based on K-means clustering

2020 ◽  
Vol 19 (3) ◽  
pp. 1548
Author(s):  
Xujie Tan ◽  
Seong-Yoon Shin
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Island Model ◽  
Benchmark Problems ◽  
De Algorithm ◽  
Mutation Strategies ◽  
The Individual ◽  
And Control

<p>Differential evolution (DE) is a highly effective evolutionary algorithm. However, the performance of DE depends on strategies and control parameters. The combination of many strategies helps balance the exploitation and exploration of DE. In this study, a multi-population based on<em> k</em>-means clustering is proposed to realize an ensemble of multiple strategies, thereby resulting in a new DE variant, namely KSDE, where similar individuals in the population implement the same mutation strategies, and dissimilar subpopulations migrate information through the soft island model (SIM). Firstly, the population is virtually divided into <em>k</em> subpopulations by the <em>k</em>-means clustering algorithm. Secondly, the individual specific mutation scheme is selected from a strategy pool by random method. Finally, the migration of subpopulation information is done using soft island model. The performance of the KSDE algorithm is evaluated on 13 benchmark problems. The experiments show that KSDE algorithm improves the performance of the DE algorithm.<strong></strong></p>

scholarly journals Improved ensemble of differential evolution variants

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256206
Author(s):  
Juan Yao ◽  
Zhe Chen ◽  
Zhenling Liu
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
State Of The Art ◽  
Population Based ◽  
Size Reduction ◽  
Real Parameter ◽  
De Algorithm ◽  
External Archive ◽  
Single Objective ◽  
Event Triggered

In the field of Differential Evolution (DE), a number of measures have been used to enhance algorithm. However, most of the measures need revision for fitting ensemble of different combinations of DE operators—ensemble DE algorithm. Meanwhile, although ensemble DE algorithm may show better performance than each of its constituent algorithms, there still exists the possibility of further improvement on performance with the help of revised measures. In this paper, we manage to implement measures into Ensemble of Differential Evolution Variants (EDEV). Firstly, we extend the collecting range of optional external archive of JADE—one of the constituent algorithm in EDEV. Then, we revise and implement the Event-Triggered Impulsive (ETI) control. Finally, Linear Population Size Reduction (LPSR) is used by us. Then, we obtain Improved Ensemble of Differential Evolution Variants (IEDEV). In our experiments, good performers in the CEC competitions on real parameter single objective optimization among population-based metaheuristics, state-of-the-art DE algorithms, or up-to-date DE algorithms are involved. Experiments show that our IEDEV is very competitive.

scholarly journals Differential Evolution Dynamics Modeled by Longitudinal Social Network

2017 ◽  
Vol 26 (3) ◽  
pp. 523-529 ◽  
Author(s):  
Lenka Skanderova ◽  
Tomas Fabian ◽  
Ivan Zelinka
Keyword(s):  
Social Network ◽  
Differential Evolution ◽  
Short Interval ◽  
Optimal Solution ◽  
Population Based ◽  
De Algorithm ◽  
Parent Selection ◽  
Evolution Dynamics ◽  
Population Improvement ◽  
Population Based Algorithm

AbstractDifferential evolution (DE) is a population-based algorithm using Darwinian and Mendel principles to find out an optimal solution to difficult problems. In this work, the dynamics of the DE algorithm are modeled by using a longitudinal social network. Because a population of the DE algorithm is improved in generations, each generation of DE algorithm is represented by one short-interval network. Each short-interval network is created by individuals contributing to population improvement. On the basis of this model, a new parent selection in the mutation operation is presented and a well-known benchmark set CEC 2013 Special Session on Real-Parameter Optimization (including 28 functions) is used to evaluate the performance of the proposed algorithm.

scholarly journals Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110144
Author(s):  
Qianqian Zhang ◽  
Daqing Wang ◽  
Lifu Gao
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Inverse Kinematics ◽  
Differential Evolution Algorithm ◽  
Weight Coefficient ◽  
Adaptive Mutation ◽  
Feasible Region ◽  
Serial Manipulators ◽  
De Algorithm ◽  
Self Adaptive

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

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