Dual preferred learning embedded asynchronous differential evolution with adaptive parameters for engineering applications

Sadhana ◽  
2021 ◽  
Vol 46 (3) ◽  
Author(s):  
Vaishali Yadav ◽  
Ashwani Kumar Yadav ◽  
Manjit Kaur ◽  
Dilbag Singh
2012 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Stefanus Eko Wiratno ◽  
Nurdiansyah Rudi ◽  
Budi Santosa

This research focuses on the development of Differential Evolution(DE) algorithmto solve m-machine flow shopscheduling problems with respect to both makespan and total flow time. Development of DE algorithm is done by modifyingthe adaptive parameter determination procedure in order to change the value of adaptive parameters in each generation,adding local search strategy to the algorithm in order to improve the quality of the resulting solutions, as ewell as modifyingthe crossover in order to reduce computation time. The result indicates that the proposed DE algorithm has proven to bebetter than the original DE algorithm, Genetic Algorithm (GA), and for certain cases it also out performs Multi-ObjectiveAnt Colony System Algorithm (MOCSA).


2013 ◽  
Vol 303-306 ◽  
pp. 1674-1677 ◽  
Author(s):  
Liang Shun Ma ◽  
Kuang Rong Hao ◽  
Yong Sheng Ding

Parallel robot inverse kinematics solution is relatively easier to obtain than forward kinematics forward, this paper assumes that the forward kinematics of six-DOF parallel robot is given then using the kinematics inverse solution to approximate the forward solution. To improve efficiency and stability of the approximation, an adaptive parameters differential evolution algorithm is proposed and applied to the forward kinematics problem. The experimental results show that the modified algorithm can gain the forward kinematics of parallel robot efficiently.


MENDEL ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 17-24
Author(s):  
Mark Wineberg ◽  
Samuel Opawale

Over the years, a lot of research has gone into the creation of different mutation operators and adaptive parameters for differential evolution (DE). However, the literature is fairly quiet about automatically setting population size and completely silent about varying the selection operator used within DE. In this paper, we steal a page from CMA-ES/IPOP: using ES-style µ+λ selection, which selects across the entire population, in place of more individualistic DE selection with its use of local selection on the target and its child. We find that the most effective choice of selection can depend on the function being optimized, although for most of the functions we tested, the original DE selection was preferable. When adding IPOP style restarting, EqualFunValHist is the most applicable of the stagnation criteria, and it is used to trigger the doubling of the population size upon restart. The initial population size is set to the same as CMA-ES. Here we find, that the restartable DE behave as well and better as regular DE with population size set as lower than the default settings used.


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