scholarly journals A Class of Parameter Estimation Methods for Nonlinear Muskingum Model Using Hybrid Invasive Weed Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Aijia Ouyang ◽  
Li-Bin Liu ◽  
Zhou Sheng ◽  
Fan Wu
Keyword(s):  
Optimization Problem ◽  
Estimation Methods ◽  
Invasive Weed Optimization ◽  
Third Order ◽  
Invasive Weed ◽  
Muskingum Model ◽  
Unconstrained Optimization Problem ◽  
Discretization Schemes ◽  
Nonlinear Muskingum Model ◽  
Parameter Estimation Methods

Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameterθto approximate the nonlinear Muskingum model based on general trapezoid formulas. The accuracy of these schemes is second order, ifθ≠1/3, but interestingly whenθ=1/3, the accuracy of the presented scheme gets improved to third order. Then, the present schemes are transformed into an unconstrained optimization problem which can be solved by a hybrid invasive weed optimization (HIWO) algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the present methods. The numerical results substantiate the fact that the presented methods have better precision in estimating the parameters of nonlinear Muskingum models.

scholarly journals Synthesis of Phase-Only Reconfigurable Linear Arrays Using Multiobjective Invasive Weed Optimization Based on Decomposition

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Liu ◽  
Yong-Chang Jiao ◽  
Ya-Ming Zhang ◽  
Yan-Yan Tan
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Amplitude Distribution ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Linear Arrays ◽  
Reconfigurable Array ◽  
Multiobjective Optimization Problems ◽  
Antenna Array Synthesis

Synthesis of phase-only reconfigurable array aims at finding a common amplitude distribution and different phase distributions for the array to form different patterns. In this paper, the synthesis problem is formulated as a multiobjective optimization problem and solved by a new proposed algorithm MOEA/D-IWO. First, novel strategies are introduced in invasive weed optimization (IWO) to make original IWO fit for solving multiobjective optimization problems; then, the modified IWO is integrated into the framework of the recently well proved competitive multiobjective optimization algorithm MOEA/D to form a new competitive MOEA/D-IWO algorithm. At last, two sets of experiments are carried out to illustrate the effectiveness of MOEA/D-IWO. In addition, MOEA/D-IWO is compared with MOEA/D-DE, a new version of MOEA/D. The comparing results show the superiority of MOEA/D-IWO and indicate its potential for solving the antenna array synthesis problems.

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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