Invasive Weed Optimization Algorithm Research Articles

Improved Cascade Chaotic Invasive Weed Optimization Algorithm (ICCIWO), application to controller tuning and optimization

ABSTRACT An Improved Cascade Chaotic Invasive Weed Optimization Algorithm (ICCIWO) is proposed, employing cascade chaotic maps in the structure of a metaheuristic optimisation algorithm. The main aim of this modification is to provide a new algorithm for tuning the parameters of controllers. Conventionally, invasive weed optimisation algorithms (IWO) have been utilised in the literature to solve optimisation problems. The IWO has merits, namely fast convergence and high extraction capability. However, the IWO suffers from a lack of population diversity and poor exploration. Two novel improvements are proposed here to eliminate these disadvantages, based on utilising chaotic behaviours in the population generation and in the standard deviation update rule. The final proposed mechanism is applied to tune the parameters of a PID controller and solve several benchmark optimisation problems. Simulation analysis compares the performance of the proposed method with a recent similar approach. The simulation results validate the high performance of the proposed novel cascade chaotic scheme.

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.

Development of the Multi-Objective Invasive Weed Optimization Algorithm in the Integrated Water Resources Allocation Problem

Development of the Multi-Objective Invasive Weed Optimization Algorithm in the Integrated Water Resources Allocation Problem

Improved Bare Bones Particle Swarm Optimization for DNA Sequence Design.

DNA computing has efficient computational power, but requires high requirements on the DNA sequences used for coding, and reliable DNA sequences can effectively improve the quality of DNA encoding. And designing reliable DNA sequences is an NP problem, because it requires finding DNA sequences that satisfy multiple sets of conflicting constraints from a large solution space. To better solve the DNA sequence design problem, we propose an improved bare bones particle swarm optimization algorithm (IBPSO). The algorithm uses dynamic lensing opposition-based learning to initialize the population to improve population diversity and enhance the ability of the algorithm to jump out of local optima; An evolutionary strategy based on signal-to-noise ratio(SNR) distance is designed to balance the exploration and exploitation of the algorithm; Then an invasive weed optimization algorithm with niche crowding(NCIWO) is used to eliminate low-quality solutions and improve the search efficiency of the algorithm. In addition, we introduce the triplet-bases unpaired constraint to further improve the quality of DNA sequences. Finally, the effectiveness of the improved strategy is demonstrated by ablation experiments; and the DNA sequences designed by our algorithm are of higher quality compared with those generated by the four advanced algorithms.

New Meta-heuristic - Based Approach for Identification and Control of Stable and Unstable Systems

Nowadays, the use of meta-heuristic algorithms (MAs) for tackling complicated engineering issues has shown significant promise, therefore applying MAs to optimum model parameters and PID parameters can be quite beneficial. As a result, this paper looks at the capabilities of four recently released resilient MAs in optimizing model parameters and PID parameters for various system behaviors. Hence, these four meta-heuristic algorithms are used such as Ant Colony Optimization (ACO), Cultural Algorithm (CA), Invasive Weed Optimization (IWO), and Black Hole Algorithm (BHA). The key contribution of this study is the employment of many meta-heuristics at the same time with the same objective function while taking into consideration each algorithm parameters for identification and control, then compared to traditional techniques such as Least square (LS) and Reference Model (RM). Thus, the most efficient algorithm is the one that yields the lowest cost function, has the lowest standard deviation (SD), and uses the least amount of CPU time. Regarding identification, simulation findings showed that CA algorithm has the best cost, lowest standard deviation (SD) and fewest CPU time 2.7838e-13, 7.1108e-13 and 3.1395(s), respectively. As for control system, it is shown that created intelligent-based controllers are more dependable than reference model controllers in stabilizing the behaviors of the various examined processes, with the IWO algorithm finds the best gains of PID and converges the fastest with best cost 3.2905e-10 and CPU time 48.8732(s). Moreover, ACO and BHA both failed to achieve satisfactory results in terms of accuracy and CPU time compared to others algorithms. Additionally, studies also showed that optimization methods has good performance, resilient and effective.

Detection of intrusions in clustered vehicle networks using invasive weed optimization using a deep wavelet neural networks

A VANET is a kind of IMS, or intelligent transportation system, that connects moving vehicles. Guaranteeing timely and accurate information sharing between vehicles and infrastructure helps make roads safer and more efficient. Wireless connections are used for data transmission in VANET, making security a critical design challenge. At the same time, the energy constrained ability of the vehicles in VANET creates energy efficiency as a challenging problem. In this aspect, this study presents an effective invasive weed optimization algorithm based energy efficient clustering with deep wavelet neural network based intrusion detection (IWOEEC-DWNN) technique for VANET. The clustered VANET is the target of the IWOEEC-DWNN approach, which seeks to detect any intrusions that may occur. In addition, the IWOEEC-DWNN method builds clusters through an IWO-based cluster technique's optimum cluster head (CH) selection and cluster design. In addition, the DWNN model is employed as a classification model to determine the occurrence of intrusions in the VANET. The design of IWOEEC-DWNN technique considerably helps to accomplish security and energy efficiency. Extensive computations were performed to test the efficacy of the IWOEEC-DWNN method, and the findings showed that it was superior to competing methods on a number of different metrics.

A robust possibilistic multi-echelon multi-product multi-period production-inventory-routing problem considering internal operations of cross-docks: Case study of FMCG supply chain

Integrated and simultaneous decision-making is essential for the production, reproduction, storage, distribution, and cross-docking, especially in Fast-Moving Consumer Goods (FMCG) supply chains. In this paper, a production-inventory-routing problem (PIRP) is essential. We have addressed the PIRP considering four-echelon multi-product, multiple periods, and the reverse flow of defective products. The PIRP also considers the consolidation of the tasks of cross-docks. Robust possibilistic programming (RPP) and possibilistic chance-constrained programming (PCCP) model the demand uncertainty. Uncertainty performance metrics evaluate the proposed solution approaches. Meta-Heuristics, including Teaching-Learning-based Optimization (TLBO) and Invasive weed optimization (IWO) algorithms, solve the deterministic equivalence of RPP and PCCP. Results demonstrate the benefits of the developed models and the robustness of the solution procedures in a real-life FMCG supply chain.

Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping

The groundwater contained in aquifers is among the most important water supply resources, especially in semi-arid and arid regions worldwide. This study aims to evaluate and compare the prediction capability of two well–known models, support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), combined with a genetic algorithm (GA), invasive weed optimization (IWO), and teaching–learning-based optimization (TLBO) algorithms in groundwater potential mapping (GPM) the Azraq Basin in Jordan. The hybridization of the SVM and ANFIS models with the GA, IWO, and TLBO algorithms results in six models: SVM–GA, SVM–IWO, SVM–TLBO, ANFIS–GA, ANFIS–IWO, and ANFIS–TLBO. A database consisting of well data containing 464 wells with 12 predictive factors was developed for the groundwater potential mapping (GPM) of the study area. Of the 464 well locations, 70% (325 locations) were assigned for the training set and the rest (139 locations) for the validation set. The correlation between the 12 predictive factors and the well locations is analyzed using the frequency ratio (FR) statistical model. An area under receiver operating characteristic (AUROC) curve was used to evaluate and compare the models. According to the results, the SVM-based hybrid models outperformed other ANFIS hybrid models in the learning (training) and validation phases. The SVM–GA and SVM–TLBO hybrid models showed AUROC values of 0.984 and 0.971, respectively, in the training and validation phases. Moreover, the ANFIS–GA and ANFIS–TLBO hybrid models showed an AUROC of 0.979 and 0.984 in the training phase and an AUROC of 0.973 and 0.984 in the validation phase, respectively. The SVM–IWO and ANFIS–IWO hybrid models showed the lowest AUROC. This study demonstrated the more efficient results of the SVM-based hybrid models in comparison with the ANFIS-based hybrid models in terms of accuracy and modeling speed.

Multi-product and multi-period maritime oil logistics model with a cooperative approach among hubs

In this paper, the modelling of maritime oil logistics problem in non-cooperative and cooperative approaches is addressed. The problem includes several hubs; each hub has vessels and boats for maritime transport. Rigs are supplied in a time window. In the non-cooperative model, each hub supplies its own rigs. In the cooperative model, it is possible to share resources among hubs. Genetic algorithm (GA) and invasive weed optimisation (IWO) are used as solution approaches. To demonstrate the efficiency of solution methods, 15 numerical examples are designed and the results are compared with GAMS. The results indicate that the IWO algorithm has high efficiency in obtaining near-optimal solutions in a shorter CPU time. Also, cost is decreased significantly when a cooperative approach is applied. Using optimisation models lead to a considerable decrease in expenditures of maritime oil logistics. The proposed model can be widely used for oil and gas companies involved in offshore logistics. [Received: March 21, 2022; Accepted: July 20, 2022].

A new hybrid evolutionary algorithm for multi-objective optimal power flow in an integrated WE, PV, and PEV power system

• A novel hybrid multi-objective evolutionary algorithm (MOEA) was developed based on the decomposition and invasive weed optimization (IWO) techniques for solving the MOOPF problem. • The conventional MOOPF problem was modified by taking stochastic WE, PV, and PEV powers into consideration. • Modeling the stochastic nature of WE, PV, and PEV sources was done using probability distribution functions (PDFs) and calculating its uncertainty cost was done with Monte Carlo simulations. • Using an effective constraint handling method (CHM) known as the superiority of feasible solution (SF) was done to address difficult constraints. • The one-way analysis of variance (ANOVA) test was deployed to give a certain level of confidence to our study. This study proposes a novel hybrid multi-objective evolutionary algorithm (MOEA) based on decomposition and invasive weed optimization (IWO) algorithm for the optimal power flow (OPF) problem in transmission networks. The conventional OPF is modified as a stochastic OPF with the integration of WE, PV, and PEV systems uncertainty. This paper proposes a new constraint-handling method (CHM) that adds the penalty adaptively and avoids parameter reliance on penalty computation. The selection features in the IWO method are deployed to improve the diversity of the proposed method. The OPF problem is represented as a multi-objective optimization (MOO) problem using four objectives: generation cost, emission, power loss, and voltage variation. The generation costs of WE, PV, and PEV sources are evaluated using Monte Carlo simulations to mitigate the whole cost and the influence of intermittency of these systems is investigated in terms of affordability and feasibility. Weibull, lognormal, and normal probability distribution functions (PDFs) are employed to define the uncertainty of WE, PV, and PEV sources respectively. The suggested method's viability is evaluated on IEEE 57 and IEEE 118- bus systems under all conceivable scenarios. In addition, one-way ANOVA test, a statistical approach, is used to evaluate the superiority of the suggested algorithm.

A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms

Hodgkin-type lymphoma is a disease with unique histological, immunophenotypic, and clinical features. This disease occurs in nearly 30% of all lymphomas. Its treatable is high. However, the treatment plan is specified after the stage and risk status are determined. For this reason, it is an important process for doctors to decide on the stage of the disease correctly. Some of the data used for this decision are the patient's history, detailed physical examination, laboratory findings, imaging methods and bone marrow biopsy results. Hybrid FDG-PET is the other method used in the medical world. This method is used in diagnosis, evaluation of response given to treatment, staging and restaging process. However, it is radiation-based. Therefore it has the possibility of producing undesirable results in the future. In this study, an artificial intelligence-based computer-assisted decision support system is done to reduce the number of used medical methods and radiation exposure. Data were obtained from the NCBI-GEO dataset. The evaluation of these data, which contains missing values, is handled in two ways. Firstly, samples with missing values in the initial evaluation are deleted from the dataset. Then, these data are trained with “trainlm” function in artificial neural network architecture. However, reducing the error value of the estimates is important. For this, the artificial neural network architecture is retrained with the artificial bee colony algorithm, particle swarm optimization algorithm and invasive weed algorithm, respectively. Secondly, the same operations are performed again on the dataset containing missing values. As a result of the training, the maximum performance was obtained for invasive weed and particle swarm optimization algorithms with 1,45547E+14 and 1,23103E+14 average error rates, respectively.

A local supply chain inventory planning with varying perishability rate product: A case study

Motivated by a real-life case in the dairy supply chain, we study the management of deteriorating inventories in an existing three-echelon supply chain network with a local retailer with multiple customers. Like many industries, the focal company categorizes the customers into two different clusters in the supply chain downstream with different, uncertain demand functions: (i) many, small, ordinary customers; (ii) few, large, premier customers. The focal company intends to determine the optimal inventory policy while the deterioration rate of the product is time sensitive. The problem is formulated using two concepts: 1) queuing theory and finite-horizon Semi-Markov process to characterize the structure of the optimal inventory policy; 2) integrated inventory system in the supply chain at the network level. We formulate the problem mathematically and show that the model is nonlinear and nonconvex. Therefore, the model cannot be solved by using commercial optimization software products to reach global optimal. Consequently, two solution approaches are designed to solve the problem: (i) a modified version of the Invasive Weed Optimization algorithm, named adaptive Invasive Weed Optimization, which is designed, tuned and validated (using well-known algorithms) to tackle the problem; (ii) an Adaptive Heuristic Method. We show that adaptive Invasive Weed Optimization is superior in the quality of solutions while Adaptive Heuristic Method is faster. Various sensitivity analyses applied to the real data show when facing both perishability and uncertainty, the effect of perishability outweighs the effect of uncertainty.

An effective discrete invasive weed optimization algorithm for multi-AGVs dispatching problem with specific cases in matrix manufacturing workshop

This paper mainly studies the dynamic multiple automatic guided vehicles dispatching problem (AGVDP) with special cases in matrix manufacturing workshop. The purpose is to introduce dynamic strategy into the static dispatching process to improve the ability to deal with special situations in the scheduling process. To solve this problem, a mathematical model is formulated based on actual production. A new discrete invasive weed optimization algorithm (DIWO) with rescheduling strategy is proposed. A dynamic rescheduling strategy is proposed to improve the ability of scheduling system. A strategy of setting emergency waiting time is proposed based on dynamic rescheduling strategy. A heuristic is proposed to dynamically update the task list, which can effectively update the current task list, solving the problem of task update in special cases and the deletion of the task that has been dispatched. An insertion based local search is used to increase the local search ability. A comprehensive simulation experiment based on the 110 instances from an actual production factory validates the effectiveness of the proposed algorithm.

Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimization

Abstract In order to classify data and improve extreme learning machine (ELM), this study explains how a hybrid optimization-driven ELM technique was devised. Input data are pre-processed in order to compute missing values and convert data to numerical values using the exponential kernel transform. The Jaro–Winkler distance is used to identify the relevant features. The feed-forward neural network classifier is used to categorize the data, and it is trained using a hybrid optimization technique called the modified enhanced Invasive Weed, a meta heuristic algorithm, and Cuckoo Search, a non-linear optimization algorithm ELM. The enhanced Invasive Weed optimization (IWO) algorithm and the enhanced Cuckoo Search (CS) algorithm are combined to create the modified CSIWO. The experimental findings presented in this work demonstrate the viability and efficacy of the created ELM method based on CSIWO, with good experimental result as compared to other ELM techniques.

Electrocardiograph signal recognition using wavelet transform based on optimized neural network

<span lang="EN-US">Due to the growing number of cardiac patients, an automatic detection that detects various heart abnormalities has been developed to relieve and share physicians’ workload. Many of the depolarization of ventricles complex waves (QRS) detection algorithms with multiple properties have recently been presented; nevertheless, real-time implementations in low-cost systems remain a challenge due to limited hardware resources. The proposed algorithm finds a solution for the delay in processing by minimizing the input vector’s dimension and, as a result, the classifier’s complexity. In this paper, the wavelet transform is employed for feature extraction. The optimized neural network is used for classification with 8-classes for the electrocardiogram (ECG) signal this data is taken from two ECG signals (ST-T and MIT-BIH database). The wavelet transform coefficients are used for the artificial neural network’s training process and optimized by using the invasive weed optimization (IWO) algorithm. The suggested system has a sensitivity of over 70%, a specificity of over 94%, a positive predictive of over 65%, a negative predictive of more than 93%, and a classification accuracy of more than 80%. The performance of the classifier improves when the number of neurons in the hidden layer is increased.</span>

Research on optimization operation technology of QT oil pipeline based on the Heuristic algorithm

Global environmental problems have become increasingly prominent, and China, as one of the world’s major powers, should take action. China promises to achieve a “carbon peak” by 2030, and carbon dioxide emissions will no longer increase, and will gradually decrease after reaching the peak. To achieve “carbon neutrality” by 2060, all the carbon dioxide emissions will be offset by tree planting, energy saving and emission reduction. The optimization of pipeline energy consumption is also associated with it. In recent years, the transportation mode of the QT oil pipeline has changed from normal temperature transportation to heating transportation. The energy consumption of this transportation method mainly comes from heating furnaces and pumps. In order to reduce energy consumption and find a suitable pipeline operation plan, this article optimizes and analyzes the transformed QT oil pipeline under the premise of ensuring safe production. Based on programming software, this article establishes a corresponding mathematical model of energy consumption of the QT oil pipeline, and uses artificial bee colony algorithm, invasive weed algorithm and optimization algorithm based on biogeography to solve the model. The article innovatively introduces the speed of the variable frequency pump as a variable to study the energy consumption optimization problem of the oil pipeline, analyze the practical application of the oil pipeline of the QT oil pipeline, and obtains the best plan for the optimized operation of the oil pipeline of the QT oil pipeline It provided research basis and played a role in promoting the country’s dual-carbon goals.

SentiWordNet Ontology and Deep Neural Network Based Collaborative Filtering Technique for Course Recommendation in an E-Learning Platform

The expansion of the population that wants to learn online is growing due to several e-learning platforms, which help innovate and suggest courses to learners. Several techniques are devised for determining optimal courses for the learner. In recent days, researchers began to utilize recommendation systems in e-learning. This paper devises a novel technique for course recommendation to students in an e-learning platform, which helps learners select the best course. Here, the Butterfly Weed Optimization (BWO) is newly devised by combining Invasive Weed Optimization (IWO) and Butterfly Optimization Algorithm (BOA). At first, the process is performed by inputting the data to the Course subscription matrix for constructing the matrix based on learner interest and courses. Here, course grouping is performed using Interval type-2 Fuzzy Local Enhancement Based Rough K-means Clustering. Furthermore, the course is matched with input data based on entropy and angular distance. Finally, the sentiment classification is performed using the Ontology-based approach SentiWordNet and Deep Neural Network (DNN). Here, the DNN is trained with the proposed BWO algorithm, and thus the course recommendation is attained by offering a suitable course recommendation to learners.

Trajectory planning algorithm based on MIWO used for nondestructive testing of EAST divertor

To improve the efficiency of testing the debonding defects on the joint interface of DOME in EAST divertor, this paper designed a time-optimal trajectory planning algorithm based on MIWO. First, invasive weed optimization(IWO) algorithm is choosed because it is simpler to operate than most intelligent algorithm. Then, we propose an improved scheme based on IWO algorithm to accelerate its convergence rate while ensure the search depth by adding inertia factor. Finally, the calculation program with GUI interface is written and the time optimal trajectory is obtained by using the Modified IWO(MIWO) algorithm and the simulations are operated. The results show that the time optimal algorithm improves the efficiency of DOME detection and the MIWO algorithm speeds up the convergence, and the calculation results are verified to be correct.

Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification

Website phishing is a cyberattack that targets online users for stealing their sensitive data containing login credential and banking details. The phishing websites appear very similar to their equivalent legitimate websites for attracting a huge amount of Internet users. The attacker fools the user by offering the masked webpage as legitimate or reliable for retrieving its important information. Presently, anti-phishing approaches necessitate experts to extract phishing site features and utilize third-party services for phishing website detection. These techniques have some drawbacks, as the requirement of experts for extracting phishing features is time consuming. Many solutions for phishing websites attack have been presented, such as blacklist or whitelist, heuristics, and machine learning (ML) based approaches, which face difficulty in accomplishing effectual recognition performance due to the continual improvements of phishing technologies. Therefore, this study presents an optimal deep autoencoder network based website phishing detection and classification (ODAE-WPDC) model. The proposed ODAE-WPDC model applies input data pre-processing at the initial stage to get rid of missing values in the dataset. Then, feature extraction and artificial algae algorithm (AAA) based feature selection (FS) are utilized. The DAE model with the received features carried out the classification process, and the parameter tuning of the DAE technique was performed using the invasive weed optimization (IWO) algorithm to accomplish enhanced performance. The performance validation of the ODAE-WPDC technique was tested using the Phishing URL dataset from the Kaggle repository. The experimental findings confirm the better performance of the ODAE-WPDC model with maximum accuracy of 99.28%.

An Improved Deep Neural Network Model of Intelligent Vehicle Dynamics via Linear Decreasing Weight Particle Swarm and Invasive Weed Optimization Algorithms.

We propose an improved DNN modeling method based on two optimization algorithms, namely the linear decreasing weight particle swarm optimization (LDWPSO) algorithm and invasive weed optimization (IWO) algorithm, for predicting vehicle’s longitudinal-lateral responses. The proposed improved method can restrain the solutions of weight matrices and bias matrices from falling into a local optimum while training the DNN model. First, dynamic simulations for a vehicle are performed based on an efficient semirecursive multibody model for real-time data acquisition. Next, the vehicle data are processed and used to train and test the improved DNN model. The vehicle responses, which are obtained from the LDWPSO-DNN and IWO-DNN models, are compared with the DNN and multibody results. The comparative results show that the LDWPSO-DNN and IWO-DNN models predict accurate longitudinal-lateral responses in real-time without falling into a local optimum. The improved DNN model based on optimization algorithms can be employed for real-time simulation and preview control in intelligent vehicles.