Introduction to Invasive Weed Optimization Method

2020 ◽  
pp. 203-214
Author(s):  
Dilip Kumar ◽  
B. G. Rajeev Gandhi ◽  
Rajib Kumar Bhattacharjya
Keyword(s):  
Optimization Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

2020 ◽  
pp. 036119812096794
Author(s):  
Eslam Mohammed Abdelkader ◽  
Osama Moselhi ◽  
Mohamed Marzouk ◽  
Tarek Zayed
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Statistical Significance ◽  
Optimization Method ◽  
Bridge Management ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Elman Neural Network ◽  
Existing Bridges ◽  
Value Decomposition

Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges face budgetary constraints. This state of affairs necessitates the development of a computer vision-based method to alleviate shortcomings in visual inspection-based methods. In this context, the present study proposes a three-tier method for the automated detection and recognition of bridge defects. In the first tier, singular value decomposition ([Formula: see text]) is adopted to formulate the feature vector set through mapping the most dominant spatial domain features in images. The second tier encompasses a hybridization of the Elman neural network ([Formula: see text]) and the invasive weed optimization (I[Formula: see text]) algorithm to enhance the prediction performance of the ENN. This is accomplished by designing a variable optimization mechanism that aims at searching for the optimum exploration–exploitation trade-off in the neural network. The third tier involves validation through comparisons against a set of conventional machine-learning and deep-learning models capitalizing on performance prediction and statistical significance tests. A computerized platform was programmed in C#.net to facilitate implementation by the users. It was found that the method developed outperformed other prediction models achieving overall accuracy, F-measure, Kappa coefficient, balanced accuracy, Matthews’s correlation coefficient, and area under curve of 0.955, 0.955, 0.914, 0.965, 0.937, and 0.904, respectively as per cross validation. It is expected that the method developed can improve the decision-making process in bridge management systems.

scholarly journals A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

2020 ◽  
Vol 26 (4) ◽  
pp. 643-661
Author(s):  
Eslam Mohammed Abdelkader ◽  
Osama Moselhi ◽  
Mohamed Marzouk ◽  
Tarek Zayed
Keyword(s):  
Optimization Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Multi Objective

scholarly journals Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Yanyan Tan ◽  
Xue Lu ◽  
Yan Liu ◽  
Qiang Wang ◽  
Huaxiang Zhang
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Multiobjective Optimization Problems ◽  
Distinct Features

In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D-IWO in solving these complicated multiobjective optimization problems.

scholarly journals The Hybrid Invasive Weed Optimization-Shuffled Frog-leaping Algorithm Applied to Optimal Design of Frame Structures

2019 ◽  
Author(s):  
Ali Kaveh ◽  
Siamak Talatahari ◽  
Nima Khodadadi
Keyword(s):  
Optimization Algorithm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Optimization Method ◽  
Population Based ◽  
Frame Structures ◽  
Invasive Weed Optimization ◽  
Shuffled Frog Leaping Algorithm ◽  
Invasive Weed ◽  
Shuffled Frog Leaping

In this article, an efficient hybrid optimization algorithm based on invasive weed optimization algorithm and shuffled frog-leaping algorithm is utilized for optimum design of skeletal frame structures. The shuffled frog-leaping algorithm is a population-based cooperative search metaphor inspired by natural memetic, and the invasive weed optimization algorithm is an optimization method based on dynamic growth of weeds colony. In the proposed algorithm, shuffled frog-leaping algorithm works to find optimal solution region rapidly, and invasive weed optimization performs the global search. Different benchmark frame structures are optimized using the new hybrid algorithm. Three design examples are tested using the new method. This algorithm converges to better or at least the same solutions compared the utilized methods with a smaller number of analyses. The outcomes are compared to those obtained previously using other recently developed meta-heuristic optimization methods.

scholarly journals Pattern Synthesis of Time-Modulated Array Antenna Based on an Improved Invasive Weed Optimization Method

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Zhenkai Zhang ◽  
Xinxing Liu ◽  
Bing Zhang ◽  
Hailin Li
Keyword(s):  
Objective Function ◽  
Pulse Sequence ◽  
Optimization Method ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Invasive Weed Optimization ◽  
Pattern Synthesis ◽  
Invasive Weed ◽  
Objective Function Values ◽  
Novel Strategy

In this paper, pattern synthesis through time-modulated linear array is studied, and a novel strategy for harmonic beamforming in time-modulated array is proposed. The peak side lobe level is designed as optimization objective function, and the switch-on time sequence of each element is selected as optimization variable. An improved invasive weed optimization (IWO) algorithm is developed in order to determine the optimal parameters describing the pulse sequence used to modulate the excitation weights of array elements. Representative results are reported and discussed to point out potentialities and advantages of the proposed approach, which can obtain lower objective function values.

Yagi-Uda Antenna Optimization Based on Invasive Weed Optimization Method

2011 ◽  
Vol 31 (8) ◽  
pp. 571-577 ◽  
Author(s):  
Yan Li ◽  
Feng Yang ◽  
Jun OuYang ◽  
Haijing Zhou
Keyword(s):  
Optimization Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed

scholarly journals Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller

2019 ◽  
Vol 6 (3) ◽  
pp. 284-295 ◽  
Author(s):  
Mojgan Misaghi ◽  
Mahdi Yaghoobi
Keyword(s):  
Standard Deviation ◽  
Chaos Theory ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Optimization Problems ◽  
Optimization Method ◽  
Population Based ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Chaotic Mapping

Abstract Weed is a phenomenon which is looks for optimality and finds the best environment for life and quickly adapts itself to environmental conditions and resists changes. Considering these features, a powerful optimization algorithm is developed in this study. The invasive weed optimization algorithm (IWO) is a population-based evolutionary optimization method inspired by the behavior of weed colonies. In this paper, the IWO algorithm is based on chaos theory. Among parameters of weed optimization algorithm, standard deviation affects the performance of the algorithm significantly. Therefore, chaotic maps are used in the standard deviation parameter. Performance of the chaotic invasive weed development method is investigated on five benchmark functions, using logistic chaotic mapping. Additionally, the problem of setting the PID controller parameters for a DC motor using the proposed method is discussed. The statistical results on optimization problems show that the improved chaotic weed algorithm has gained fast convergence rate and high accuracy. Highlights Improved Invasive weed optimization Algorithm (IWO) based on Chaos theory. Improved setting the parameters of PID controller uses Chaotic IWO Algorithm.

A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

2019 ◽  
pp. -1--1
Author(s):  
Eslam Abdelkader ◽  
Osama Moselhi ◽  
Mohamed Marzouk ◽  
Tarek Zayed
Keyword(s):  
Optimization Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Multi Objective
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