Weed Optimization Algorithm Research Articles

Reservoir operation management using a new hybrid algorithm of Invasive Weed Optimization and Cuckoo Search Algorithm

Abstract Water scarcity throughout the world has led to major difficulties and complexities in managing water demands. These challenges gravitate towards the development of efficient methods for optimal reservoir operation. The present study aims to introduce a hybrid approach which integrates Invasive Weed Optimization (IWO) and Cuckoo Search Algorithm (CSA), with an objective to minimize the deficits for Indira Sagar Reservoir (ISR), India. To prevail over the limitations of the Weed Optimization Algorithm (WOA) and CSA, a critical comparison has been made in the study. The hybrid approach has improved the performance by 5 and 9% as compared to WOA and CSA, respectively. For the reservoir system, the Cv for 10 random runs was computed to be 0.0303 using the hybrid model, whereas for WOA and CSA, Cv was 0.22034 and 0.30698, respectively. Based on the performance measuring indices, results revealed that the hybrid model is more reliable and sustainable with the minimum error between release and demand. In addition, results reveal that the deficits have been reduced by 62% on average for the considered study period using the hybrid approach. Therefore, the results show that the proposed hybrid model has considerable potential to be used as an optimizer for complex reservoir operation problems.

Optimization model for integrated river basin management with the hybrid WOAPSO algorithm

This work examines the effectiveness of a newly-developed optimization framework for river basin management. The proposed framework relies on the newly developed WOAPSO algorithm, which is a hybrid metaheuristic algorithm combining two conventional metaheuristic algorithms, namely the weed optimization algorithm (WOA) and the particle swarm optimization algorithm (PSO). Two case studies are presented in this study to evaluate the performance of the WOAPSO algorithm. The first case study consists of a ten-reservoir river basin example which compares the performance and reliability of the hybrid WOAPSO algorithm with that of linear programing (LP), non-linear programing (NLP), WOA, and the PSO algorithm. Results indicate the hybrid WOAPSO finds solutions meeting downstream water demands with 99.94% of reliability (with respect to the global optimum, as derived by LP) in the ten-reservoir system. It outperforms the WOA and PSO, which feature lower reliabilities than that achieved by WOAPSO. The second case study demonstrates failure of the conventional NLP optimization scheme in solving a real-world three-reservoir hydropower optimization problem which maximizes the efficiency index of hydropower production. The newly-introduced WOAPSO algorithm minimizes the objective function with superior efficiency compared with those of the WOA and PSO, in terms of the convergence rate and the achieved best values of the objective function. Furthermore, the WOAPSO is proven more reliable for solving complex multi-reservoir systems within the context of integrated river basin management than classic and evolutionary optimization algorithms.

ENHANCED ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS

In this paper, Enhanced Aggressive Weed Optimization (EWO) algorithm is applied to solve the optimal reactive power Problem. Aggressive Weed Optimization is a stochastic search algorithm that imitate natural deeds of weeds in colonize and detection of appropriate place for growth and reproduction. Enhanced Aggressive Weed Optimization (EWO) algorithm is based on hybridization of genetic algorithm with weed optimization algorithm which refers combination of crossover and mutation of genetic algorithm, and by the use of the cross factor new species are arisen. Proposed Enhanced Aggressive Weed Optimization (EWO) algorithm has been evaluated in standard IEEE 118 & practical 191 bus test systems. Simulation results show that our projected approach outperforms all the entitled reported algorithms in minimization of real power loss and voltage profiles are within the specified limits.

A new approach for simplification and control of linear time invariant systems

This study presents a new approach for system simplification and control. This approach is based on the behaviour of growth and reproduction of weed plants namely Invasive Weed Optimization (IWO). The micro system/simplified model of large scale single-input single-output (SISO) continuous time system is attained by optimizing a predefined objective/fitness function by weed optimization algorithm. Performance of the proposed approach is analysed in terms of transient and frequency response parameters such as rise time, peak time, maximum overshoot, gain margin and phase margin, etc. This analysis reveals that the results of this proposed approach are commensurable with other available approaches. Additionally, the application of proposed approach is explored in control of a micro system based on system simplification. The control action is obtained by achieving the unknown parameters of the proportional integral derivative (PID) controller using IWO. Further, the concept of error minimization is being utilised to obtain parameters of PID controller. The simulation results reveal that the PID controller designed by proposed approach has no affect on change of the system. The PID controller for a micro system (second order) exhibits satisfactory performance on the first order simplified system, therefore changing the system does not affect the performance of PID controller. As the proposed approach is being utilised for both micro system’s simplification as well as in controller design, therefore it may be applied in various applications of Microsystems’s analysis and design.

Hybrid Invasive Weed Optimization Algorithm for Parameter Inversion Problems

A hybrid invasive weed optimization (HIWO) algorithm based on the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is proposed for the problems on parameter inversion of the nonlinear models of sun shadow with integer variables in the study. Our presented algorithm can take full advantage of the local search ability of BFGS algorithm and the global search ability of invasive weed optimization (IWO) algorithm. The HIWO algorithm can not only reverse the date of sun shadow model successfully, but also conquer the weaknesses that the classic mathematical methods are hard to address integer nonlinear optimization problems by utilizing integers in some random variables from algorithms. The results of numerical experiments demonstrate that the HIWO algorithm has not only high computing accuracy, but also fast convergence speed. It can effectively improve the accuracy and efficiency of the techniques of sun shadow location, and afford an effective and efficient technique to handle the issues of integer parameter inversion in engineering applications.

Optimization of energy management and conversion in the water systems based on evolutionary algorithms

In this article, an application of weed optimization algorithm (WOA) for reservoir operation was proposed. In addition, genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) have been used as successful algorithms in such engineering application for optimizations in order to introduce a comprehensive comparison analysis with the results of WOA. Two case studies were considered in this research. The aim of the first case study was to minimize irrigation water deficits for Aydoughmoush dam in Iran. Results showed that average solution of the operation rule of the WOA is close to the global solution and that reliability index and resiliency index for water supply, based on WOA, are higher than the GA and PSOA. Also, the operation rule achieved by the WOA has well-supplied water to match the water demands for the operation period (1991–2000). The second case study was related to minimize the electricity power deficits in Karun-4 reservoir. In fact, the water should be released such that the electricity power generation is maximized or the electricity power deficit should be minimized. Results showed that the operation rule for the water achieved by the WOA produces more electrical power than GA and PSOA. Furthermore, the average solution accomplished by WOA is very close to the global solution. Different indexes were used for evaluating the proposed methods, namely reliability index, vulnerability index, and resiliency index. For the first case study, the proposed WOA could achieve 98% reliability which is outperformed the other GA and PSOA methods. On the other hand, for the second case study, the proposed WOA method could attain electricity power 12 and 15% more than the one generated while using the GA and PSOA methods. Thus, WOA has high potential to be applied for achieving optimal operation rule for dam and reservoir operation and better water resources management and plan.

Two swarm intelligence-based approaches for the <i>p</i>-centre problem

The p-centre problem is an important facility location problem. In this problem, the objective is to find a set Y of p vertices on an undirected weighted graph G = (V, E) in such a way that Y ⊆ V and the maximum distance over all the distances from vertices to their closest vertices in Y is minimised. The vertices in set Y are called centres. In this paper, we have proposed two swarm intelligence-based approaches for the p-centre problem. The first approach is based on artificial bee colony (ABC) algorithm, whereas the latter approach is based on invasive weed optimisation (IWO) algorithm. The ABC algorithm and IWO algorithm are relatively new metaheuristic techniques inspired respectively from collective intelligent behaviour shown by honeybees while foraging and the sturdy process of weed colonisation and dispersion in an ecosystem. Computational results on the well-known benchmark instances of p-centre problem show the effectiveness of our approaches in finding high quality solutions.

Closure to “Parameter Estimation of Extended Nonlinear Muskingum Models with the Weed Optimization Algorithm” by Farzan Hamedi, Omid Bozorg-Haddad, Maryam Pazoki, Hamid-Reza Asgari, Mehran Parsa, and Hugo A. Loáiciga

Closure to “Parameter Estimation of Extended Nonlinear Muskingum Models with the Weed Optimization Algorithm” by Farzan Hamedi, Omid Bozorg-Haddad, Maryam Pazoki, Hamid-Reza Asgari, Mehran Parsa, and Hugo A. Loáiciga

An Optimized Wideband High Directive Microstrip Fractal Patch Antenna based on Near-Zero-Index Metamaterial Superstrate

ABSTRACTIn this article, a wideband near-zero-index metamaterial (NZIM) unit cell, for gain improvement of a broadband microstrip fractal patch antenna is proposed and designed. The near-zero refractive index characteristics of metamaterial superstrate is used, which gathers the radiated waves from the source antenna and collimates them toward the normal direction. The reference fractal patch antenna operates at a relatively wide frequency range covering from 2.38 to 3.61 GHz, corresponding to a 40% fractional bandwidth. In order to effectively enhance the broadside gain of the antenna, a proposed unit cell is optimized using invasive weed optimization (IWO) algorithm to have the widest bandwidth at the required frequency band, in which the refractive index is less than unity (approaches zero). A prototype of the antenna was fabricated and tested. Measured results are in good agreement with the simulation. The measured −10 dB relative impedance bandwidth for the loaded antenna with NZIM is found to be 31%. The maximum measured broadside gain is about 3.1 dB higher than that of the reference antenna without NZIM, which corresponds to 86% aperture efficiency.

Economic production quantity model with imperfect products and random order frequency under due date and limited storage capacity

This research is motivated by the fact that production systems are often not perfect and constraints on delivery due date, storage capacity and order frequency are an integral part of many real-world inventory systems. In this regard, this paper deals with a multi-product economic production quantity (EPQ) problem in an imperfect production system with rework where De Novo programming concept has been used in the model formulation. The non-linear programming model constructed here attempts to minimize the total costs of production system where constraints on delivery due date, order frequency as well as storage space are captured in the model. Due to the complexity of the proposed EPQ model to be tackled in real-sized problems, genetic algorithm (GA) and invasive weed optimization (IWO) algorithm are adopted to find near-optimal solutions in reasonable computational times. The Taguchi method for robust parameters design is then used to obtain the optimal level of parameters of the developed algorithms. The practical aspects of the proposed model and the performance of the algorithms for the considered problem are tested through solving a set of randomly generated problem instances. Finally, the results are examined by the statistical analysis and entropy method which specifies the supremacy of developed IWO in comparison with GA.

Hybrid Invasive Weed Optimization Algorithm for Parameter Estimation of Pharmacokinetic Model

Given that the traditional methods of estimating pharmacokinetic parameters are constrained by the sensitivity of their initial value and incapability of evolutionary algorithm to determine search range, this paper proposes a hybrid invasive weed optimization (HIWO) algorithm by combining the Hooke–Jeeves (HJ) and invasive weed optimization (IWO) algorithm. Using the HIWO algorithm for the parameter optimization by the experiment of extravascular administration two-compartment model, we can see that the proposed method is not only better than traditional feathering method (FM) in terms of numerical stability, but also better than HJ and IWO in terms of error minimization. The experimental results show that HIWO algorithm is a feasible method to optimize the pharmacokinetic parameters, which has higher precision and stronger robustness than the other techniques.

A parallel improved IWO algorithm on GPU for solving large scale global optimization problems

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

Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm

The optimal hydropower operation of reservoir systems is known as a complex nonlinear nonconvex optimization problem. This paper presents the application of invasive weed optimization (IWO) algorithm, which is a novel evolutionary algorithm inspired from colonizing weeds, for optimal operation of hydropower reservoir systems. The IWO algorithm is used to optimally solve the hydropower operation problems for both cases of single reservoir and multi reservoir systems, over short, medium and long term operation periods, and the results are compared with the existing results obtained by the two most commonly used evolutionary algorithms, namely, particle swam optimization (PSO) and genetic algorithm (GA). The results show that the IWO is more efficient and effective than PSO and GA for both single reservoir and multi reservoir hydropower operation problems.

Band Selection and Dimension Estimation for Hyperspectral Imagery—a New Approach Based on Invasive Weed Optimization

Currently, hyperspectral images have potential applications in many scientific areas due to the high spectral resolution. Extracting suitable and adequate bands/features from high dimensional data is a crucial task to classify such data. To overcome this issue, dimension reduction techniques have direct effects to improve the efficiency of classifiers on hyperspectral images. One common approach for decreasing the dimensionality is the feature/band selection by considering the optimum dimensionality of the hyperspectral imagery. In this paper, a new method was proposed to select optimal band for classification application, based on a metaheuristic Invasive Weed Optimization (IWO) algorithm. In this regard, the K-nearest neighbour (K-NN) technique was used as the classifier. Moreover, as a by-product of our band selection method, a new method was proposed to estimate an optimum dimension of the reduced hyperspectral images for better classification. Experimental results over three real-world hyperspectral datasets clearly showed that the proposed IWO-based band selection algorithm of this study led to the significant progress in selecting suitable bands for classification applications and estimation of optimum dimensionality of these datasets. In this regard, the overall accuracy (OA) of classification of the proposed IWO-based band selection algorithm was 92.02, 93.57, and 89.72 % for each dataset, respectively. Moreover, results reveal the superiority of the proposed IWO-based band selection algorithm against the other algorithms including GA, SA, ACO, and PSO for band selection purpose.

Weed Optimization Algorithm for Optimal Reservoir Operation

AbstractThis study introduces the weed optimization algorithm (WOA) to optimal reservoir operation. The WOA is a metaheuristic optimization method inspired by weeds’ life cycle. The effectiveness o...

An Efficient and Effective Algorithm for Large Scale Global Optimization Problems

Invasive weed optimization (IWO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm are inclined to fall into local optimum with lower convergence accuracy when separately used to deal with large scale global optimization (LSGO) problems. In order to fully utilize the advantages of these two intelligent algorithms and complement each other, following the idea of portfolio optimization, this paper correspondingly adjusts and improves the quantum models of IWO and QPSO, organically integrates the two algorithms, and proposes the quantum-behaved invasive weed optimization (QIWO) algorithm. This mixed algorithm can achieve the purpose of information exchange and cooperative search through alternate search enables the make algorithm converge to the optimal solution quickly, properly overcoming the defects of falling into local optimum and premature convergence. Test results of 20 LSGO functions show that compared with other algorithms, QIWO has stronger global optimization capability, faster convergence speed and higher convergence accuracy.

Improved kernel possibilistic fuzzy clustering algorithm based on invasive weed optimization

Fuzzy c-means (FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means (PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based on invasive weed optimization (IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed optimization (IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel method to make the input data from the sample space map into the high-dimensional feature space. Then, the sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved algorithm is used to cluster data. The simulation results of the University of California-Irvine (UCI) data sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster accuracy and faster convergence speed than the PFCM algorithm.

Stock Price Prediction Model Based on IWO Neural Network and its Applications

In view of the deficiency of the standard back-propagation algorithm based on steepest descent method, a new kind of optimization strategy called invasive weed optimization (IWO) algorithm is introduced into the training process of feed-forward neural networks, and then a prediction model based on IWO feed-forward neural network (IWO-NN) is given. By the dynamic adjustment of standard deviation of the distribution of offspring individuals in IWO, the local convergence speed of networks is improved and the defect of trapping into a local optimum is reduced. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has better global astringency, robustness, and it is insensitive to initial values.

A simulated weed colony system with subregional differential evolution for multimodal optimization

This article proposes a two-stage hybrid multimodal optimizer based on invasive weed optimization (IWO) and differential evolution (DE) algorithms for locating and preserving multiple optima of a real-parameter functional landscape in a single run. Both IWO and DE have been modified from their original forms to meet the demands of the multimodal problems used in this work. A p-best crossover operation is introduced in the subregional DEs to improve their exploitative behaviour. The performance of the proposed algorithm is compared with a number of state-of-the-art multimodal optimization algorithms over a benchmark suite comprising 21 basic multimodal problems and seven composite multimodal problems. Experimental results suggest that the proposed technique is able to provide better and more consistent performance over the existing well-known multimodal algorithms for the majority of test problems without incurring any serious computational burden.

Solution to dynamic economic dispatch problem using modified invasive weed optimisation with dual mutation strategy

An efficient modified invasive weed optimisation (MIWO) approach for solving the dynamic economic dispatch (DED) problem with valve-point effect is presented in this paper. The invasive weed optimisation (IWO) algorithm is a stochastic method for global optimisation in real problems. The proposed algorithm implements a radical dual mutation strategy suitable for solving high dimensional problems which have non-homogeneous search spaces with respect to each decision variable. In this proposed algorithm the standard deviation (σ) is taken as a time-variant function and changes according to the iteration number by which the new seeds produced get closer to the global solution. To validate the feasibility and effectiveness of the proposed approach, it is demonstrated for a five-unit system and two different ten-unit systems. The proposed modified IWO is able to provide better solution than the results obtained using previously published methods in terms of minimum cost.