Local Search in Evolutionary Algorithms: The Impact of the Local Search Frequency

2006 ◽  
pp. 359-368 ◽  
Author(s):  
Dirk Sudholt
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
The Impact

Comparative Study of Evolutionary Algorithms for Parameter Identification of an Impact Oscillator

2014 ◽  
Author(s):  
Amit Banerjee ◽  
Issam Abu Mahfouz
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Parameter Identification ◽  
Optimization Techniques ◽  
System Response ◽  
Impact Oscillator ◽  
Swarm Optimization ◽  
System Parameters ◽  
Highly Nonlinear ◽  
The Impact

The use of non-classical evolutionary optimization techniques such as genetic algorithms, differential evolution, swarm optimization and genetic programming to solve the inverse problem of parameter identification of dynamical systems leading to chaotic states has been gaining popularity in recent years. In this paper, three popular evolutionary algorithms — differential evolution, particle swarm optimization and the firefly algorithm are used for parameter identification of a clearance-coupled-impact oscillator system. The behavior of impacting systems is highly nonlinear exhibiting a myriad of harmonic, low order and high order sub-harmonic resonances, as well as chaotic vibrations. The time-history simulations of the single-degree-of-freedom impact oscillator were obtained by the Neumark-β numerical integration algorithm. The results are illustrated by bifurcation graphs, state space portraits and Poincare’ maps which gives valuable insights on the dynamics of the impact system. The parameter identification problem relates to finding one set of system parameters given a chaotic or periodic system response as a set of Poincaré points and a different but known set of system parameters. The three evolutionary algorithms are compared over a set of parameter identification problems. The algorithms are compared based on solution quality to evaluate the efficacy of using one algorithm over another.

scholarly journals Hybridization of evolutionary algorithms and local search by means of a clustering method

2006 ◽  
Vol 36 (3) ◽  
pp. 534-545 ◽  
Author(s):  
A.C. Martinez-Estudillo ◽  
C. Hervas-Martinez ◽  
F.J. Martinez-Estudillo ◽  
N. Garcia-Pedrajas
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Clustering Method
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