Invasive Weed Optimization Algorithm Research Articles

Quality Analysis of Extreme Learning Machine based on Cuckoo Search and Invasive Weed Optimization

This paper explicates hybrid optimization driven Extreme Machine Learning (ELM) strategy is developed with feed forward neural network (FFNN) for the classification of data and improving ELM. The pre-processing of input data is carried for the missing value imputation and transformation of data into numerical value using exponential kernel transform. The significant feature is determined using the Jaro–Winkler distance. The classification of data is done using the FFNN classifier, which is trained with the help of the hybrid optimization algorithm, namely developed modified Cuckoo Search and Invasive Weed Optimization (CSIWO) ELM. The modified CSIWO is devised by integrating the modified Cuckoo search (CS) algorithm and Invasive Weed Optimization (IWO) algorithm. The experimental results proposed in this paper show the feasibility and effectiveness of the developed CSIWO ELM method with encouraging performance compared with other ELM methods.

An improved invasive weed optimization enabled Shepard convolutional neural network for classification of breast cancer

AbstractOne of the most predominant types of cancer affecting women is breast cancer. In computer‐assisted analysis of pathology, the most crucial task is classifying breast cancer automatically from the histopathological images. Most of the patients experienced everlasting effects and loss of life because of the absence of timely detection and thereby delay in treatment. Thus, the timely detection as well as precise identification of breast cancer using the histopathological image is highly required. An improved invasive weed water wave optimization (IIWWWO) algorithm is devised here, which is employed in training the Shepard convolutional neural networks (ShCNN) classifier. The developed optimization algorithm combines the improved invasive weed optimization algorithm and water wave optimization algorithm. Initially, the Bayesian filtering process is applied in the input image to remove the noises present in the input image. Moreover, the Hessian‐based Laplacian of Gaussian texture feature model is utilized in segmenting blood cells for an effective classification process. The shape features, namely shape area, diameter, and perimeter, are effectively extracted for better performance. Besides, ShCNN classifier is the most important one for classifying breast cancers. The proposed IIWWWO optimization technique is utilized in tuning the employed classifier termed as ShCNN classifier. Classification of breast cancer is accomplished using two levels, such as malignant and benign. The developed IIWWWO‐based ShCNN model is compared with various metrics, like accuracy, true negative rate (TNR) and true positive rate (TPR). The developed technique used to classify breast cancer obtained better accuracy of 95.83%, maximal TPR of 94.72%, and high TNR of 93.15%.

A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions.

Industrialization and population growth have been accompanied by many problems such as waste management worldwide. Waste management and reduction have a vital role in national management. The presents study represents a multi-objective location-routing problem for hazardous wastes. The model was solved using Non dominated Sorting Genetic Algorithm-II, Multi-Objective Particle Swarm Optimization, Multi-Objective Invasive Weed Optimization, Pareto Envelope-based Selection Algorithm, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer algorithms. The findings revealed that the Multi-Objective Invasive Weed Optimization algorithm was the best and the most efficient among the algorithms used in this study. Obtaining income from the incineration of the wastes and reducing the risk of COVID-19 infection are the first innovation of the present study, which considered in the presented model. The second innovation is that uncertainty was considered for some of the crucial parameters of the model while the robust fuzzy optimization model was applied. Besides, the model was solved using several meta-heuristic algorithms such as Multi-Objective Invasive Weed Optimization, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer, which were rarely used in literature. Eventually, the most efficient algorithm was identified by comparing the considered algorithms.

A comprehensive analysis of grid-based wind turbine layout using an efficient binary invasive weed optimization algorithm with levy flight

Wind energy has attracted great attention in recent years due to the increasing demand for alternative energy sources. Gathering the maximum amount of energy from wind energy is directly related to the layout of wind turbines in wind farm. This study focuses on grid-based wind turbine layout problem in an area of 2 km × 2 km. In order to solve this problem, 9 novel grids of 11 × 11, 12 × 12, … and 19 × 19 are proposed in this paper in addition to 10 × 10 and 20 × 20 turbine grids which have already used in the literature. A new repair operator is recommended, taking into account the distance constraint between two adjacent cells in turbine layout problem, and thus the obtained solutions are made feasible. Furthermore, a levy flight-based IWO (LFIWO) algorithm is developed to optimize the layout of the turbines in wind farm. The basic IWO algorithm and the proposed LFIWO algorithm are compared on 11 different turbine layouts. Experimental studies are carried out for both algorithms under equal conditions with the help of 10 different binary versions. According to the comparisons performed using 11 different grids, LFIWO demonstrates a much superior success than the IWO algorithm. In addition, the proposed 19 × 19 grid reveals the best success among the other grids. When compared to the other studies in the literature, it is seen that LFIWO surpasses the other algorithms in terms of solution quality. As a result, it can be clearly stated that the proposed binary version of LFIWO is a competitive and effective binary algorithm.

New Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection

Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology for induction-motor fault detection. Some of the motor parameters such as the stator currents and vibration signals provide a great deal of information about the motor’s conditions. Therefore, these signals of the motor were selected to test the proposed model. The induction motor was assessed in a laboratory under healthy, mechanical, and electrical faults with different loadings. In this study a new hybrid model was developed using the collected signals, an optimal features selection mechanism is proposed, and machine learning classifiers were trained for fault classification. The procedure is to extract some statistical features from the raw signal using Matching Pursuit (MP) and Discrete Wavelet Transform (DWT). Then, the Invasive Weed Optimization algorithm (IWO)-based optimal subset was selected to reduce the data dimension and increase the average accuracy of the model. The optimal subset of features was fed into three classification algorithms: k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), which were trained using k-fold cross-validation to distinguish between the induction motor faults. A similar strategy was performed by applying the Genetic Algorithm (GA) to compare with the performance of the proposed method. The suggested fault detection model’s performance was evaluated by calculating the Receiver Operation Characteristic (ROC) curve, Specificity, Accuracy, Precision, Recall, and F1 score. The experimental results have proved the superiority of IWO for selecting the discriminant features, which has achieved more than 99.7% accuracy. The proposed hybrid model has successfully proved its robustness for diagnosing the faults under different load conditions.

A novel model for sustainable waste collection arc routing problem: Pareto-based algorithms.

Municipal solid waste (MSW) management is known as one of the most crucial activities in municipalities that requires large amounts of fixed/variable and investment costs. The operational processes of collection, transportation and disposal include the major part of these costs. On the other hand, greenhouse gas (GHG) emission as environmental aspect and citizenship satisfaction as social aspect are also of particular importance, which are inevitable requirements for MSW management. This study tries to develop a novel mixed-integer linear programming (MILP) model to formulate the sustainable periodic capacitated arc routing problem (PCARP) for MSW management. The objectives are to simultaneously minimize the total cost, total environmental emission, maximize citizenship satisfaction and minimize the workload deviation. To treat the problem efficiently, a hybrid multi-objective optimization algorithm, namely, MOSA-MOIWOA is designed based on multi-objective simulated annealing algorithm (MOSA) and multi-objective invasive weed optimization algorithm (MOIWOA). To increase the algorithm performance, the Taguchi design technique is employed to set the parameters optimally. The validation of the proposed methodology is evaluated using several problem instances in the literature. Finally, the obtained results reveal the high efficiency of the suggested model and algorithm to solve the problem.

An Improved Intrusion Weed Optimization Algorithm for Node Location in Wireless Sensor Networks

The distribution optimization of WSN nodes is one of the key issues in WSN research, and also is a research hotspot in the field of communication. Aiming at the distribution optimization of WSN nodes, the distribution optimization scheme of nodes based on improved invasive weed optimization algorithm(IIWO) is proposed. IIWO improves the update strategy of the initial position of weeds by using cubic mapping chaotic operator, and uses the Gauss mutation operator to increase the diversity of the population. The simulation results show that the algorithm proposed in this paper has a higher solution quality and faster convergence speed than IWO and CPSO. In distribution optimization example of WSN nodes, the optimal network coverage rate obtained by IIWO is respectively improved by 1.82% and 0.93% than the IWO and CPSO. Under the condition of obtaining the same network coverage rate, the number of nodes required by IIWO is fewer.

Improved Invasive Weed Optimization Algorithm for Global Maximum Power Point Tracking of PV Array Under Partial Shading Conditions

Photovoltaic (PV) array under partial shading conditions (PSCs) has several maximum power points (MPPs) on the power-voltage curve of the PV array. These points; have a unique global peak (GP) and the others are local peaks (LPs). This paper aims to study an improved version of a heuristic optimization technique namely, Invasive Weed Optimization (IWO) to track the global maximum power point (GMPP) of a PV array which is an important issue. The proposed improved IWO (IIWO) algorithm modifies IWO to speed up the convergence and make the system more efficient. In addition to study the effect of changing input parameters of IIWO on its performance. An overall statistical evaluation of IIWO, with standard IWO and Particle Swarm Optimization (PSO) is executed under different shading conditions. The simulation results show that IIWO has faster and better convergence as it can reach the GMPP in less time compared with other techniques.

Swarm intelligence, exact and matheuristic approaches for minimum weight directed dominating set problem

There exist numerous heuristic and exact approaches in the literature for addressing minimum weight dominating set problem (MWDS) on vertex-weighted undirected graphs which is a well known NP-hard problem. However, little attention has been paid to its counterpart in vertex-weighted directed graphs called minimum weight directed dominating set problem (MWDDS) despite its use in modeling real-world applications involving directed interactions. As directed graphs can model undirected graphs, MWDDS can be considered as a generalization of MWDS, and hence, MWDDS is also NP-hard. In this paper, we present two approaches based on swarm intelligence, one approach based on integer linear programming (ILP) and one matheuristic approach to address the MWDDS. These approaches are the first approaches for MWDDS in their respective categories. One of our swarm intelligence approach is based on artificial be colony (ABC) algorithm, whereas the other is based on invasive weed optimization (IWO) algorithm. Both these approaches are hybridized with problem specific heuristics and a local search mechanism. We have evaluated the performance of our approaches on benchmark instances derived from the standard benchmark instances of MWDS. Computational results show the effectiveness of our approaches.

Maximum likelihood DOA estimation based on improved invasive weed optimization algorithm and application of MEMS vector hydrophone array

<abstract><p>Direction of arrival (DOA) estimation based on Maximum Likelihood is a common method in array signal processing, with many practical applications, but the huge amount of calculation limits the practical application. To deal with such an Maximum Likelihood (ML) DOA estimation problem, firstly, the DOA estimation model with ML for acoustic vector sensor array is developed, where the optimization standard in various cases can be unified by converting the maximum of objective function to the minimum. Secondly, based on the Invasive Weed Optimization (IWO) method which is a novel biological evolutionary algorithm, a new Improved IWO (IIWO) algorithm for DOA estimation of the acoustic vector sensor array is proposed by using ML estimation. This algorithm simulates weed invasion process for DOA estimation by adjusting the non-linear harmonic exponent of IWO algorithm adaptively. The DOA estimation accuracy has been improved, and the computation of multidimensional nonlinear optimization for the ML method has been greatly reduced in the IIWO algorithm. Finally, compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) method and Tuna Swarm Optimization(TSO) algorithm, numerical simulations show that the proposed algorithm has faster convergence rate, improved accuracy in terms of Root Mean Square Error (RMSE), lower computational complexity and more robust estimation performance for ML DOA estimation. The experiment with tracking the orientation of the motorboat by Microelectronic mechanical systems (MEMS) vector hydrophone array shows the superior performance of proposed IIWO algorithm in engineering application. Therefore, the proposed ML-DOA estimation with IIWO algorithm can take into account both resolution and computation. which can meet the requirements of real-time calculation and estimation accuracy in the actual environment.</p></abstract>

Hybrid Invasive Weed Improved Grasshopper Optimization Algorithm for Cloud Load Balancing

In cloud computing, the processes of load balancing and task scheduling are major concerns as they are the primary mechanisms responsible for executing tasks by allocating and utilizing the resources of Virtual Machines (VMs) in a more optimal way. This problem of balancing loads and scheduling tasks in the cloud computing scenario can be categorized as an NP-hard problem. This problem of load balancing needs to be efficiently allocated tasks to VMs and sustain the trade-off among the complete set of VMs. It also needs to maintain equilibrium among VMs with the objective of maximizing throughput with a minimized time span. In this paper, a Hybrid Invasive Weed Improved Grasshopper Optimization Algorithm-based-efficient Load Balancing (HIWIGOA-LB) technique is proposed by adopting the merits of the Invasive Weed Optimization Algorithm (IWOA) into the Grasshopper Optimization Algorithm (GOA) for determining the near-optimal solution that facilitates optimal load balancing. In particular, the random walk strategy is adopted to prevent the local point of optimality problem. It also utilized the strategy of grouping to modify the exploitation coefficient associated with the traditional GOA for balancing the rate of exploration and exploitation. The simulation investigations of the proposed HIWIGOA-LB scheme confirmed its better performance in minimizing the make span and response time by 13.21% and 16.71%, with a maximized throughput of 19.28%, better than the baseline approaches considered for investigation.

E-puck motion control using multi-objective particle swarm optimization

This article describes the velocity-based motion and orientation control method for a differential-driven two-wheeled E-puck Robot (DDER) using the Multi-Objective Particle Swarm Optimization (MPSO) algorithm in the Virtual Robot Experimentation Platform (V-REP) software environment. The wheel velocities data and Infra-Red (IR) sensors reading make the multi-objective fitness functions for MPSO. We use front, left, and right IR sensors reading and right wheel velocity data to design the first fitness function for MPSO. Similarly, the front, left, and right IR sensors reading, and left wheel velocity data have been taken for making the second fitness function for MPSO. The multi-objective fitness functions of MPSO minimize the motion and orientation of the DDER during navigation. Due to the minimization of motion and orientation, the DDER covers less distance to reach the goal and takes less time. The Two-Dimensional (2D) and Three-Dimensional (3D) navigation results of the DDER among the scattered obstacles have been presented in the V-REP software environment. The comparative analysis with previously developed Invasive Weed Optimization (IWO) algorithm has also been performed to show the effectiveness and efficiency of the proposed MPSO algorithm.

Object Tracking Based on Modified Invasive Weed Optimization Algorithm

Object Tracking Based on Modified Invasive Weed Optimization Algorithm

Underwater source localization using time difference of arrival and frequency difference of arrival measurements based on an improved invasive weed optimization algorithm

In this study, we propose an underwater localization method based on an improved invasive weed optimization algorithm to accurately locate moving sources in underwater sensor networks. First, the Lévy flight model is introduced into the invasive weed optimization algorithm to enhance its global search ability and avoid falling into local optima. At the same time, under the condition that the observed noise of each observation is Gaussian noise and does not consider the influence of other error factors, the localization error is adopted as the objective function to obtain an initial estimate for the unknown source parameter. Then, the obtained initial estimates of the target position and velocity as well as the target parameter error are utilized to construct a new localization model. Finally, the precise position of the source and its velocity are obtained according to the weighted least square method. The performance of the algorithm is verified by comparing it with the Cramér–Rao Lower Bound (CRLB). Results from simulations indicate that the algorithm proposed in this paper has excellent localization accuracy compared to existing methods and achieves results close to the CRLB.

A novel Invasive Weed Optimization with levy flight for optimization problems: The case of forecasting energy demand

Energy is very important nowadays and it has become essential for human life. More amount of energy is necessary for a community with increased living standards and population. This causes an increase in energy consumption. Energy has become one of the most significant problems across the world today; therefore, it should be generated at the best level. Excessive energy generation makes countries lose money while less amount of energy generation causes crises. Therefore, countries need to adjust their energy demand optimally. It is possible to estimate energy demands of countries by using various applications. This study proposes a new improved algorithm for linear regression models to forecast the energy demand of Turkey. The selected algorithm is Invasive Weed Optimization (IWO) algorithm which has been developed with levy flight called LF-IWO. In the linear regression model, input parameters included data regarding Turkey’s gross domestic product (GDP), population, import and export. Turkey’s energy demand was estimated for these parameters by using the data between 1979 and 2011. The estimation results obtained from the model were compared with those of similar studies in the literature to measure the performance success of the developed algorithm.

Multi-objective reservoir operation of the Ukai reservoir system using an improved Jaya algorithm

Abstract This paper introduces an effective and reliable approach based on a multi-population approach, namely the self-adaptive multi-population Jaya algorithm (SAMP-JA), to extract multi-purpose reservoir operation policies. The current research focused on two goals: minimizing irrigation deficits and maximizing hydropower generation. Three different models were formulated. The results were compared with those for an ordinary Jaya algorithm (JA), particle swarm optimization (PSO), and an invasive weed optimization (IWO) algorithm. In Model-1, the minimum irrigation deficit obtained by SAMP-JA and JA was 305092.99 . SAMP-JA was better than JA, PSO and IWO in terms of convergence. In Model-2, the maximum hydropower generation achieved by SAMP-JA, JA and PSO was 1723.50 . When comparing the average hydropower generation, SAMP-JA and PSO performed better than JA and IWO. In terms of convergence, SAMP-JA was better than PSO. In Model-3, a self-adaptive multi-population multi-objective Jaya algorithm (SAMP-MOJA) was better than multi-objective particle swarm optimization (MOPSO) and multi-objective Jaya algorithm (MOJA) in terms of maximum hydropower generation, and MOPSO was better than SAMP-MOJA and MOJA in terms of minimum irrigation deficiency. While comparing convergence, SAMP-MOJA was found to be better than MOPSO and MOJA. Overall, SAMP-JA was found to outperform JA, POS and IWO.

Reducing the conflict of interest in the optimal operation of reservoirs by linking mesohabitat hydraulic modeling and metaheuristic optimization

Abstract The present study proposes a novel framework to optimize the reservoir operation through linking mesohabitat hydraulic modeling and metaheuristic optimization to mitigate environmental impact downstream of the reservoir. Environmental impact function was developed by mesohabitat hydraulic simulation. Then, the developed function was utilized in the structure of the reservoir operation optimization. Different metaheuristic algorithms including practice swarm optimization, invasive weed optimization, differential evolution and biogeography-based algorithm were used to optimize reservoir operation. Root mean square error (RMSE) and reliability index were utilized to measure the performance of algorithms. Based on the results in the case study, the proposed method is robust for mitigating downstream environmental impacts and sustaining water supply by the reservoir. RMSE for mesohabitats is 8%, which indicates the robustness of proposed method to mitigate environmental impacts at downstream. It seems that providing environmental requirements might reduce the reliability of water supply considerably. Differential evolution algorithm is the best method to optimize reservoir operation in the case study.

Sailfish invasive weed optimization algorithm for multiple image sharing in cloud computing

Visual cryptography (VC) is a secret sharing cryptographic technique, which permits visual information that is encrypted in such a way that the decrypted information shows the visual image. Security is the common problem that arises in the general VC technique. To secure the visual information, an effective method is proposed using a developed sailfish invasive weed optimization (SIWO) algorithm. At first, the grayscale image is shared with multiple parties, and here the shared images are encrypted and decrypted to reconstruct the original image back. The key generation is done using the developed SIWO-based data sharing approach. Here, the developed approach offered the peak signal-to-noise ratiof 34.870 and 33.711 dB for Lena and Cameraman images, respectively, and it attains better performance in terms of secret image sharing.

An Efficient IWOLRS Control Technique of Brushless DC Motor for Torque Ripple Minimization

This manuscript proposes an improved DC-DC converter framework using hybrid control algorithm for minimizing brushless DC motor (BLDC) torque ripple (TR). At first, the modeling of the brushless DC motor is intended by an enhanced Cuk converter (ECC). The function and performance of the Cuk converter are updated using application of switched inductor. In this way, the control system integrates two control loops such as speed and torque control loop, which is employed for improving BLDC performance. Therefore, the Invasive Weed Optimization (IWO) and Local Random Search (LRS) are proposed to enhance control loop operations. In the IWO algorithm, the LRS approach is used as part of the dispersion process to build up the course of action to find precision. This manuscript explores the IWO-LRS algorithm for limiting BLDC motor speed and torque error. Nevertheless, the exit from the proposed approach is subject to the speed and torque controller input. The better optimal gain parameters have been worked out for the update of the controller operation through the aid of necessary goal functions. The proposed controller topology is activated in MATLAB/Simulink site and the performance is evaluated using other existing methods, like Particle Swarm Optimization (PSO), Bacterial Foraging (BF) algorithm.

Invasive Weed Optimization Based Ransom-Ware Detection in Cloud Environment

Cloud provide support to manage, control, monitor different organization. Due to flexible nature f cloud chance of attack on it increases by means of some software attack in form of ransomware. Many of researcher has proposed various model to prevent such attacks or to identify such activities. This paper has proposed a ransomware detection model by use of trained neural network. Training of neural network was done by filter or optimized feature set obtained from the feature reduction algorithm. Paper has proposed a Invasive Weed Optimization algorithm that filter good set of feature from the available input training dataset. Proposed model test was performed on real dataset, have set sessions related to cloud ransomware attacks. Result shows that proposed model has increase the comparing parameter values.