Invasive Weed Optimization Research Articles

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

In this paper, an adaptive neuro-fuzzy based on fractional-order proportional-integral-derivative (ANFFOPID) controller with an improved slime mould algorithm (ISMA) for the two-legged robot (TLR) is proposed to achieve the minimum angular displacement error of the joint motors. Achieving such error is considered a challenging and time-consuming process due to the gain values set for the FOPID controller. Thus the neural-fuzzy network is used to provide the FOPID input signals by the adaptive magnitude gains. The adaptive mechanism depends on the ISMA to train the neural network weights. The outstanding properties of the ANFFOPID controller are evaluated by comparing the proposed controller with other existing work that is modified chaotic invasive weed optimization based on neural network (MCIWO-NN) for various walking terrains that are flat surface, stair ascending, and stair descending. Finally, the results obtained show the effectiveness of the ANFFOPID controller.

Political Improved Invasive Weed Optimization-Driven Hybrid Exemplar Technique for Video Inpainting Process

Video inpainting aspires to fill the Spatio-temporal holes in videos with probable and coherent content. This process recovers the missing content of corrupted video effectively, which is useful in many fields, including removal of watermarking and video restoration. The difficulties of creating video contents with exquisite detail while maintaining spatiotemporal coherence in the missing areas is the main difficulty in the video inpainting process. Modern studies ignore semantic structural coherence maintenance between frames in favor of using flow information to synthesize temporally smooth pixels. In this paper, Political Improved Invasive Weed Optimization (PIIWO)-based optimal exemplar is designed for the productive video inpainting process. Accordingly, the developed PIIWO algorithm is newly designed by combining Political Optimizer (PO) and Improved Invasive Weed Optimization (IIWO). Here, the inpainting results obtained from context-aware Ant Lion Gray Wolf Optimization (ALGWO)-based Markov Random Field (MRF) modeling, Whale Monarch Butterfly Optimization (Whale MBO)-based Deep Convolutional Neural Network (DCNN), K-Nearest Neighbors (KNN) with Bhattacharya distance, Bi-harmonic function modules and developed PIIWO-based exemplar model are fused using Bayes probabilistic fusion for producing the final result. Three metrics, peak signal-to-noise ratio (PSNR), second derivative like the measure of enhancement (SDME) and structural similarity (SSIM) of 40.19[Formula: see text]dB, 78.07[Formula: see text]dB and 0.9857, respectively, are used to assess the performance of the developed video inpainting technique.

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.

Optimization of water distribution networks using hybrid BBO-IWO algorithm

ABSTRACT Due to the increasing population and the pollution of existing water resources because of various factors, the need for water in the world is increasing. This requires building additional water distribution systems along with rehabilitation of existing systems. The cost of transmission and distribution constitutes more than half of the total cost of water supply systems. For this reason, by the optimum design of pipeline systems, the cost of water supply can be reduced. In this study, the optimization of pipe diameters in three networks, Two- Loop, Hanoi and GoYang, was carried out by a combination of two metaheuristic algorithms, namely Biogeography based optimization (BBO) and invasive weed optimization (IWO) under the same constraints. In addition, the Combined Gravity Network was successfully optimized with the current method for the first time in the literature.

Optimized prediction models for faulting failure of Jointed Plain concrete pavement using the metaheuristic optimization algorithms

This study aims to predict faulting failure of jointed plain concrete pavement (JPCP) using different variables. For this purpose, four feature selection methods were developed by combining the artificial neural networks (ANN) and four multi-objective metaheuristic optimization algorithms, namely, the Pareto envelope-based selection algorithm II (PESA-2), the strength Pareto evolutionary algorithm 2 (SPEA-2), multi-objective particle swarm optimization (MPSO), and multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D showed better performance compared to the other models, which identified 17 input variables affecting faulting failure. In the next step, the classic back-propagation (BP), Biogeography-based optimization (BBO), invasive weed optimization (IWO), and simulated annealing algorithm (SAA) were combined with the ANN to develop three prediction models for faulting failure. Modeling with metaheuristic optimization algorithms showed better performance than the ordinary ANN. The pavement age, cumulative average precipitation, and elasticity modulus of the concrete slab have the most significant impact on the formation and increase of faulting.

Designing an integrated humanitarian logistics network for the preparedness and response phases under uncertainty

In the current paper, a model of possibilistic location-allocation is proposed for designing a multi-period bi-objective humanitarian logistics network that pursues a reduction in the total cost and maximizes the total network coverage. The model considers the inventory management of perishable relief items and the flow of affected and wounded people in pre-disaster and post-disaster phases, simultaneously. Moreover, wounded people are categorized according to the injury severity. The uncertainty associated with key parameters is also addressed. To handle the uncertainty, a fuzzy chance-constrained programming method originated in the Me measure is used. The purpose of this measure is to prevent the extreme attitudes of the decision maker by considering the combination of possibility and necessity measures and using the optimistic-pessimistic parameter. This paper presents a solution procedure derived from a fuzzy interactive programming approach and two meta-heuristic algorithms, imperialist competitive algorithm and invasive weed optimization, so as to solve the study model. As for the validation of the proposed model and solution procedures, a number of test problems have been generated. A real-life case study is also implemented to instantiate whether the proposed model is applicable or not.

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.

Designing a sustainable bioethanol supply chain network: A combination of machine learning and meta-heuristic algorithms

Bioethanol demands have increased during the last decade due to unexpected events worldwide. It is among the renewable energy sources that are utilized to replace fossil-fuel-based energy. Designing an efficient, Sustainable Bioethanol Supply Chain Network (SBSCN) is a critical task for the government and communities to manage the bioethanol demands appropriately. The contribution of this study is threefold. First, this study project the demand using three popular machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ensemble Learning (Adaboost) methods, to find the best one to be utilized as an input to the proposed mathematical to find optimal values for strategic, planning, and tactical decision variables. Second, the unemployment rate is considered an important parameter of the model to maximize the social effects. Third, since the proposed model of this study is NP-hard, to solve the problem, the CPLEX solver is applied for small size and two meta-heuristic algorithms, including Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Invasive Weed Optimization (MOIWO) are considered for medium and large size problems to find Pareto optimal solutions. Due to the sensitivity of two meta-heuristics algorithms, the Taguchi method, such as Max Spread (MS) and Mean Ideal Distance (MID), is utilized to control the parameters of those two applied algorithms. Since North Dakota (ND) is among the states with the most potential to produce bioethanol due to its vast land area, including marginal cropland and Conservation Research Program (CRP), the proposed model has been evaluated and validated based on the ND case study. The results show that the MOIWO algorithm outperforms NSGA-II based on the proposed model and the case study of this paper. Also, this algorithm is more reliable in terms of solution quality to tackle the problem. Finally, some research directions are discussed for future studies, and managerial insights are provided. • Bioethanol consumption experienced an increasing due to unexpected worldwide event. • Utilizing machine learning approaches to estimate the bioethanol demand in N.D. • Designing sustainable bioethanol supply chain by two meta-heuristic algorithms. • Optimal locations of biorefinery, pre-processing, distribution, and demand centers. • Gradual replacement of liquid fossil fuels with bioethanol to reduce air pollutions.

Collaborative Gold Mining Algorithm: An Optimization Algorithm Based on the Natural Gold Mining Process

In optimization algorithms, there are some challenges, including lack of optimal solution, slow convergence, lack of scalability, partial search space, and high computational demand. Inspired by the process of gold exploration and exploitation, we propose a new meta-heuristic and stochastic optimization algorithm called collaborative gold mining (CGM). The proposed algorithm has several iterations; in each of these, the center of mass of points with the highest amount of gold is calculated for each miner (agent), with this process continuing until the point with the highest amount of gold or when the optimal solution is found. In an n-dimensional geographic space, the CGM algorithm can locate the best position with the highest amount of gold in the entire search space by collaborating with several gold miners. The proposed CGM algorithm was applied to solve several continuous mathematical functions and several practical problems, namely, the optimal placement of resources, the traveling salesman problem, and bag-of-tasks scheduling. In order to evaluate its efficiency, the CGM results were compared with the outputs of some famous optimization algorithms, such as the genetic algorithm, simulated annealing, particle swarm optimization, and invasive weed optimization. In addition to determining the optimal solutions for all the evaluated problems, the experimental results show that the CGM mechanism has an acceptable performance in terms of optimal solution, convergence, scalability, search space, and computational demand for solving continuous and discrete problems.

A novel adaptive invasive weed optimization technique and least square regression for harmonics minimization in standalone PV applications

SummaryA novel adaptive invasive weed optimization technique is presented in this paper for harmonics minimization in standalone system based on three‐phase two‐level voltage source inverter. The low switching frequency techniques are preferred to produce good quality waveform and at the same time to reduce the switching losses and device stresses. Nonetheless, the challenging task is to compute the firing angles from the set of highly nonlinear transcendental system of equations. The proposed technique shows faster convergence and capability to compute in wide rage of modulation index. Moreover, multiple solutions also have been obtained in most of the range of modulation index. Various cases have been evaluated and simulation results have been given to validate conceptual results. The closed loop operation and the real‐time implementation of the proposed technique under dynamic operating conditions have been verified for a three‐phase induction motor load using PI controller. The least square regression is used for real‐time implementation of the proposed technique. The TMS320F2812 digital signal processor (DSP) has been employed to generate the pulse width modulated pulses generated from the computational results. The experimental results obtained are in very close concurrence to the computational and simulation results.

Special issue on International Conference on Computing and Communication Networks

Special issue on International Conference on Computing and Communication Networks

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.

Modified invasive weed optimization-based path exploration for mobile robot

PurposeThis study aims to modify a nature-based numerical method named the invasive weed optimization (IWO) method for mobile robot path planning in various complex environments.Design/methodology/approachThe existing IWO method is quick in converging to a feasible solution but in a complex environment; it takes more time as well as computational resources. So, in this paper, the computational part of this artificial intelligence technique is modified with the help of recently developed evolution algorithms like particle swarm optimization, genetic algorithm, etc. Some conditional logic statements were used while doing sensor-based mapping for exploring complex paths. Implementation of sensor-based exploration, mathematical IWO method and prioritizing them for better efficiency made this modified IWO method take complex dynamic decisions.FindingsThe proposed modified IWO is better for dynamic obstacle avoidance and navigating a long complex map. The deviation of results in simulation and experiments is less than 5.5%, which validates a good agreement between simulation and real-time testing platforms.Originality/valueAs per a deep literature review, it has found that the proposed approach has not been implemented on the Khepera-III robot for smooth motion planning. Here a dynamic obstacle mapping feature is implemented. A method to selectively distribute seeds instead of a random normal distribution is also implemented in this work. The modified version of IWO is coded in MATLAB and simulated through V-Rep simulation software. The integration of sensors was done through logical conditioning. The simulation results are validated using real-time experiments.

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>

Intelligent metaheuristics with optimal machine learning approach for malware detection on IoT‐enabled maritime transportation systems

AbstractThe latest advancements in Internet of Things (IoT) have revolutionized the productivity of global shipping industry in the recent years. It also led to the emergence of IoT‐enabled Maritime Transportation Systems (MTS). These approaches detect the malware in network before the execution process. Various machine learning (ML) models have been proposed and designed in literature for effective malware detection. However, the existence of numerous features in the data bring dimensionality problem which can be only resolved by the use of feature selection approaches. Therefore, the current research work presents Intelligent Metaheuristics‐based Feature Selection model with Optimal ML approach for Malware Detection (IMFSOML‐MD) on IoT‐enabled MTS. Primarily, IMFSOML‐MD technique involves the design of Quantum Invasive Weed Optimization Algorithm‐based Feature Selection technique to optimally choose a subset of features. Moreover, an Optimal Wavelet Neural Network (OWNN) model is employed to perform classification process. The initial parameters of WNN model are optimally tuned with the help of Colliding Bodies Optimization algorithm thereby improving the detection performance. The proposed IMFSOML‐MD technique was experimentally validated using publicly‐available CICMalDroid2020 dataset. The results from extensive comparative analysis demonstrated the superiority of the proposed IMFSOML‐MD technique over other compared methods in terms of detection performance with maximum accuracy of 98.96%.

Multipopulation GA/IWO with Coupled Scale-Free Networks for Solving Flexible Job-Shop Scheduling Problems

In recent years, an increasing number of population-based metaheuristic algorithms have been proposed to solve the flexible job-shop scheduling problem (FJSP) according to its practicality and complexity. Most of these algorithms are single-population-based and hence are very susceptible to becoming trapped in local optimal values. Thus, multipopulation methods are widely used to improve these algorithms, resulting in multipopulation algorithms. These multipopulation algorithms have been widely studied in the context of single-layer complex networks recently. However, coupled networks used to control two (or more) algorithms simultaneously to get a better algorithm are always ignored in literature. Therefore, in this paper, using coupled scale-free networks (with different scaling exponents) to control the genetic algorithm (GA) and the invasive weed optimization (IWO) simultaneously, a multipopulation GA/IWO with coupled scale-free networks (MPGAIWO-SF) is proposed to solve FJSP. Then, we study how some parameters (e.g., subpopulation size, subpopulation number, and scaling exponent) of MPGAIWO-SF affect its performance, which is also ignored in literature. The simulation results illustrate that (1) the performance of MPGAIWO-SF is significantly improved compared with that of GA and IWO; (2) as the subpopulation size increases, the performance of MPGAIWO-SF first becomes better and then remains almost unchanged; (3) as the subpopulation number increases, the performance of MPGAIWO-SF first becomes better and then decreases rapidly; and (4) the performance of MPGAIWO-SF becomes worse slightly as the scaling exponent decreases. Finally, solving more FJSP instances, the MPGAIWO-SF with optimized parameters is compared with other related algorithms to verify its effectiveness.

On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

Predicting energy consumption has been a substantial topic because of its ability to lessen energy wastage and establish an acceptable overall operational efficiency. Thus, this research aims at creating a meta-heuristic-based method for autonomous simulation of heating and cooling loads of buildings. The developed method is envisioned on two tiers, whereas the first tier encompasses the use of a set of meta-heuristic algorithms to amplify the exploration and exploitation of Elman neural network through both parametric and structural learning. In this regard, 10 meta-heuristic were utilized, namely differential evolution, particle swarm optimization, invasive weed optimization, teaching-learning optimization, ant colony optimization, grey wolf optimization, grasshopper optimization, moth-flame optimization, antlion optimization, and arithmetic optimization. The second tier is designated for evaluating the meta-heuristic-based models through performance evaluation and statistical comparisons. An integrative ranking of the models is achieved using average ranking algorithm.

Optimizing the Maximum Lyapunov Exponent of Fractional Order Chaotic Spherical System by Evolutionary Algorithms

The main goal of this work is to optimize the chaotic behavior of a three-dimensional chaotic-spherical-attractor-generating fractional-order system and compare the results with its novel hyperchaotic counterpart. The fractional-order chaotic system is a smooth system perturbed with a hyperbolic tangent function. There are two major contributions in this investigation. First, the maximum Lyapunov exponent of the chaotic system was optimized by applying evolutionary algorithms, which are meta-heuristics search algorithms, namely, the differential evolution, particle swarm optimization, and invasive weed optimization. Each of the algorithms was populated with one hundred individuals, the maximum generation was five hundred, and the total number of design variables was eleven. The results show a massive increase of over 5000% in the value of the maximum Lyapunov exponent, thereby leading to an increase in the chaotic behavior of the system. Second, a hyperchaotic system of four dimensions was constructed from the inital chaotic system. The dynamics of the optimized chaotic and the new hyperchaotic systems were analyzed using phase portraits, time series, bifurcation diagrams, and Lyapunov exponent spectra. Finally, comparison between the optimized chaotic systems and the hyperchaotic states shows an evidence of more complexity, ergodicity, internal randomness, and unpredictability in the optimized systems than its hyperchaotic counterpart according to the analysis of their information entropies and prediction times.

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.