Cooperative coevolutionary invasive weed optimization and its application to Nash equilibrium search in electricity markets

2009 ◽  
Author(s):  
Hossein Hajimirsadeghi ◽  
Amin Ghazanfari ◽  
Ashkan Rahimi-Kian ◽  
Caro Lucas
Keyword(s):  
Nash Equilibrium ◽  
Electricity Markets ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Equilibrium Search

Multiobjective invasive weed optimization: Application to analysis of Pareto improvement models in electricity markets

2012 ◽  
Vol 12 (1) ◽  
pp. 100-112 ◽  
Author(s):  
Amir Hossein Nikoofard ◽  
Hossein Hajimirsadeghi ◽  
Ashkan Rahimi-Kian ◽  
Caro Lucas
Keyword(s):  
Electricity Markets ◽  
Invasive Weed Optimization ◽  
Pareto Improvement ◽  
Invasive Weed

Application of chaos-based chaotic invasive weed optimization techniques for environmental OPF problems in the power system

2014 ◽  
Vol 69 ◽  
pp. 271-284 ◽  
Author(s):  
Mojtaba Ghasemi ◽  
Sahand Ghavidel ◽  
Jamshid Aghaei ◽  
Mohsen Gitizadeh ◽  
Hasan Falah
Keyword(s):  
Power System ◽  
Optimization Techniques ◽  
Invasive Weed Optimization ◽  
Invasive Weed

An Adaptive Invasive Weed Optimization Algorithm

2015 ◽  
Vol 29 (02) ◽  
pp. 1559004 ◽  
Author(s):  
Shuo Peng ◽  
A.-J. Ouyang ◽  
Jeff Jun Zhang
Keyword(s):  
Optimization Algorithm ◽  
Initial Step ◽  
Local Extremum ◽  
Invasive Weed Optimization ◽  
Step Size ◽  
Invasive Weed ◽  
Adaptive Step Size ◽  
Initial Step Size ◽  
Adaptive Step ◽  
Optimization Search

With regards to the low search accuracy of the basic invasive weed optimization algorithm which is easy to get into local extremum, this paper proposes an adaptive invasive weed optimization (AIWO) algorithm. The algorithm sets the initial step size and the final step size as the adaptive step size to guide the global search of the algorithm, and it is applied to 20 famous benchmark functions for a test, the results of which show that the AIWO algorithm owns better global optimization search capacity, faster convergence speed and higher computation accuracy compared with other advanced algorithms.

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