Invasive weed optimization for model order reduction of linear MIMO systems

Abu-Al-Nadi, Dia I.; Alsmadi, Othman M.-K.; Abo-Hammour, Zaer S.; Hawa, Mohammed; Rahhal, Jamal S.

doi:10.1016/j.apm.2012.09.006

Cited by 56 publications

(12 citation statements)

References 14 publications

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“…It can be seen from Table 2 that BFGS algorithm is the worst in terms of calculation precision; the BFGS algorithm cannot find the optimum solution or approximate optimum solution in solving 20 functions. The calculation precision of NMSM is better than the one of BFGS, but the calculation precision of NMSM for most of the functions is not ideal, except for functions [16][17][18][19]. The calculation precision of IWO is better than the one of PSO; IWO has obvious advantages in terms of calculation precision for solving 11 functions.…”

Section: Experimental Results Of Benchmark Functionsmentioning

confidence: 99%

“…IWO algorithm [13] is a new bionic intelligent algorithm which simulates the spatial weed diffusion, growth, reproduction, and competitive survival of the invasive weeds [14]. IWO algorithm has been widely used in a variety of optimization problems and practical engineering problems: such as multiobjective optimization problem [15], parameter estimation of chaotic systems [16], model order reduction problem [17], global numerical optimization [18], antenna arrays problem [19,20], unit commitment problem [21], optimal power flow problem [22], flow shop scheduling problem [23], traveling salesman problem [24], and economic dispatch [25].…”

Section: Introductionmentioning

confidence: 99%

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

Ouyang¹,

Liu²,

Zhou³

et al. 2015

Mathematical Problems in Engineering

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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.

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Section: Experimental Results Of Benchmark Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ouyang¹,

Liu²,

Zhou³

et al. 2015

Mathematical Problems in Engineering

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“…Some of the advantages of IWO include the manner of reproduction, spatial dispersal, and competitive exclusion (Mehrabian and Lucas, 2006) as well as the fact that seeds and their parents are ranked together and that those with better fitness survive and become reproductive (Ahmed et al, 2014). This algorithm can benefit from combined advantages of retaining the dominant poles and the error minimization (Abu-Al-Nadi et al, 2013).…”

Section: Advantages/disadvantages Of the Models And Performance Analysismentioning

confidence: 99%

Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

Khosravi

Panahi

Bui

2018

Hydrol. Earth Syst. Sci.

148

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Abstract. Groundwater is one of the most valuable natural resources in the world (Jha et al., 2007). However, it is not an unlimited resource; therefore understanding groundwater potential is crucial to ensure its sustainable use. The aim of the current study is to propose and verify new artificial intelligence methods for the spatial prediction of groundwater spring potential mapping at the Koohdasht–Nourabad plain, Lorestan province, Iran. These methods are new hybrids of an adaptive neuro-fuzzy inference system (ANFIS) and five metaheuristic algorithms, namely invasive weed optimization (IWO), differential evolution (DE), firefly algorithm (FA), particle swarm optimization (PSO), and the bees algorithm (BA). A total of 2463 spring locations were identified and collected, and then divided randomly into two subsets: 70 % (1725 locations) were used for training models and the remaining 30 % (738 spring locations) were utilized for evaluating the models. A total of 13 groundwater conditioning factors were prepared for modeling, namely the slope degree, slope aspect, altitude, plan curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land use/land cover, rainfall, soil order, and lithology. In the next step, the step-wise assessment ratio analysis (SWARA) method was applied to quantify the degree of relevance of these groundwater conditioning factors. The global performance of these derived models was assessed using the area under the curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were carried out to check and confirm the best model to use in this study. The result showed that all models have a high prediction performance; however, the ANFIS–DE model has the highest prediction capability (AUC = 0.875), followed by the ANFIS–IWO model, the ANFIS–FA model (0.873), the ANFIS–PSO model (0.865), and the ANFIS–BA model (0.839). The results of this research can be useful for decision makers responsible for the sustainable management of groundwater resources.

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“…So far, the academia has carried out numerous studies on algorithm design and how to improve the IWO.AsIWO algorithm has a simple structure with less parameters, fast convergence and good robustness, IWO and its improved algorithms have been widely applied in many fields of natural science and engineering science, such as flow shop scheduling [19], Unit commitment problem [20], model order reduction [21], path optimization problem [22], multi-objective optimization problem [23], [24], information processing [25], [26], design of antenna arrays [27], [28]. All this has evidenced the powerful advantages and potential of IWO.…”

Section: Introductionmentioning

confidence: 99%

GPU-based variation of parallel invasive weed optimization algorithm for 1000D functions

Ouyang

Liu

et al. 2014

2014 10th International Conference on Natural Computation (ICNC)

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Considering the problems of slow convergence and easily getting into local optimum of intelligent optimization algorithms in finding the optimal solution to complex highdimensional functions, we have proposed an improved invasive weed optimization (IIWO). Concrete adjustments include setting the newborn seeds per plant to a fixed number, changing the initial step and final step to adaptive one, and re-initializing the solution which exceeds the boundary value. Meanwhile, through applying the algorithm to the GPU platform, a parallel IIWO (PIIWO) based on GPU is obtained. The algorithm not only improves the convergence, but also strikes a balance between the global and local search capabilities. The simulation results of solving on the CEC' 2010 1000-dimensional (1000D) functions, have shown that, compared with other algorithms, our designed IIWO can yield better performance, faster convergence, higher accuracy and stronger robustness; whilst the PIIWO has significant speedup than the IIWO.

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Invasive weed optimization for model order reduction of linear MIMO systems

Cited by 56 publications

References 14 publications

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

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

Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

GPU-based variation of parallel invasive weed optimization algorithm for 1000D functions

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