A Differential Evolution with Simulated Annealing Updating Method

2006 ◽  
Author(s):  
Jing-yu Yan ◽  
Qing Ling ◽  
De-min Sun
Keyword(s):  
Simulated Annealing ◽  
Differential Evolution

Differential evolution improved with self-adaptive control parameters based on simulated annealing

2014 ◽  
Vol 19 ◽  
pp. 52-67 ◽  
Author(s):  
Haixiang Guo ◽  
Yanan Li ◽  
Jinling Li ◽  
Han Sun ◽  
Deyun Wang ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Simulated Annealing ◽  
Differential Evolution ◽  
Control Parameters ◽  
Self Adaptive

scholarly journals A new hybrid differential evolution with simulated annealing and self-adaptive immune operation

2013 ◽  
Vol 66 (10) ◽  
pp. 1948-1960 ◽  
Author(s):  
Xinchao Zhao ◽  
Wenqiao Lin ◽  
Chengchi Yu ◽  
Jing Chen ◽  
Shuguang Wang
Keyword(s):  
Simulated Annealing ◽  
Differential Evolution ◽  
Adaptive Immune ◽  
Self Adaptive

Heat exchanger network synthesis combining Simulated Annealing and Differential Evolution

Energy ◽  
2019 ◽  
Vol 181 ◽  
pp. 654-664 ◽  
Author(s):  
Maria Claudia Aguitoni ◽  
Leandro Vitor Pavão ◽  
Mauro Antonio da Silva Sá Ravagnani
Keyword(s):  
Simulated Annealing ◽  
Heat Exchanger ◽  
Differential Evolution ◽  
Network Synthesis ◽  
Heat Exchanger Network ◽  
Heat Exchanger Network Synthesis

scholarly journals Path planning optimization of six-degree-of-freedom robotic manipulators using evolutionary algorithms

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090807
Author(s):  
Sandi Baressi Šegota ◽  
Nikola Anđelić ◽  
Ivan Lorencin ◽  
Milan Saga ◽  
Zlatan Car
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Differential Evolution ◽  
Robotic Manipulators ◽  
Robotic Manipulator ◽  
Degree Of Freedom ◽  
Joint Torques ◽  
Point To Point ◽  
Computation Algorithms ◽  
Six Degree Of Freedom

Lowering joint torques of a robotic manipulator enables lowering the energy it uses as well as increase in the longevity of the robotic manipulator. This article proposes the use of evolutionary computation algorithms for optimizing the paths of the robotic manipulator with the goal of lowering the joint torques. The robotic manipulator used for optimization is modelled after a realistic six-degree-of-freedom robotic manipulator. Two cases are observed and these are a single robotic manipulator carrying a weight in a point-to-point trajectory and two robotic manipulators cooperating and moving the same weight along a calculated point-to-point trajectory. The article describes the process used for determining the kinematic properties using Denavit–Hartenberg method and the dynamic equations of the robotic manipulator using Lagrange–Euler and Newton–Euler algorithms. Then, the description of used artificial intelligence optimization algorithms is given – genetic algorithm using random and average recombination, simulated annealing using linear and geometric cooling strategy and differential evolution. The methods are compared and the results show that the genetic algorithm provides best results in regard to torque minimization, with differential evolution also providing comparatively good results and simulated annealing giving the comparatively weakest results while providing smoother torque curves.

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