Invasive Weed Optimization Algorithm Research Articles

Integrated Software Development and Case Studies for Optimal Operation of Cascade Reservoir within the Environmental Flow Constraints

Reservoir optimal operation considering aquatic ecological protection is a hot topic in current research. This paper proposes an improved minimum monthly average runoff method (IMMR) for calculating environmental flow and an improved invasive weed optimization algorithm (IIWO) for optimizing complex problems. An integrated software consists of three modules, which is developed in this paper, i.e., IIWO convergence test module, environmental flow calculation module, and cascade reservoir operation module. Three test functions are included in the IIWO convergence test module. The minimum monthly average runoff method (MMR), IMMR, Tennant Method, Q90, and Q95 are included in the environmental flow calculation module. The IIWO and invasive weed optimization algorithm (IWO) are included in the cascade reservoir operation module. Wujiang River Basin in China is studied as a case in this paper. The results show that the environmental flow of cascade reservoir calculated by IMMR is 1871 m3/s, the maximum and the minimum are calculated by T-O and T-M, respectively. The power generation of cascade reservoir calculated by IWO is less than IIWO. The conclusions that IIWO has better convergence than IWO in solving cascade reservoir model, and the water volume of environmental flow has no obvious influence on cascade reservoir operation are drawn.

A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm

In recent years, large computational problems have beensolved by the distributed environment in which applications are executed in parallel. Also, lately, fog computing or edge computing as a new environment is applied to collect data from the devices and preprocessing is done before sending for main processing in cloud computing. Since one of the crucial issues in such systems is task scheduling, this issue is addressed by considering reducing energy consumption. In this study, an energy-aware method is introduced by using the Dynamic Voltage and Frequency Scaling (DVFS) technique to reduce energy consumption. In addition, in order to construct valid task sequences, a hybrid Invasive Weed Optimization and Culture (IWO-CA) evolutionary algorithm is applied. The experimental results revealed that the proposed algorithm improves some current algorithms in terms of energy consumption.

Ideal position and size selection of unified power flow controllers (UPFCs) to upgrade the dynamic stability of systems: An antlion optimiser and invasive weed optimisation Algorithm

This paper proposes a hybrid methodology to improve the dynamic strength of frameworks by optimally locating and sizing the unified power flow controller (UPFC). The proposed method combines both the antlion optimiser (ALO) and invasive weed optimisation (IWO) techniques. The advantages of the proposed methodology are enhanced searching ability, better identification of optimal solutions and greater reliability. Initially, to locate UPFCs, using the ALO technique, the most extreme power loss bus is recognised as the optimal location. The capacity required to resolve the situation is determined by the IWO technique based on the minimum power loss, voltage deviation and establishment cost of the UPFCs. The proposed system is actualised and aims at the matrix laboratory (MATLAB) environment, while the power flow security and voltage stability are evaluated on the basis of the Institute of Electrical and Electronics Engineers (IEEE) standard benchmark system. At this point, voltage stability, power loss, overloading and UPFC costs are analysed through changing the system load and generation. The proposed working method is then assessed and compared with traditional techniques.

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression.

Concrete filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge in using such structure lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (Pu) of CFST is among the most important mechanical properties. In this study, the possibility of using a feedforward neural network (FNN) to predict Pu was investigated. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN–IWO model and improve its prediction performance. The results showed that the FNN–IWO algorithm is an excellent predictor of Pu, with a value of R2 of up to 0.979. The advantage of FNN–IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R2, respectively, compared with simulation using the single FNN. Finally, the performance in predicting the Pu in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.

A novel fuzzy bi-objective vehicle routing and scheduling problem with time window constraint for a distribution system: A case study

In the process of hazardous material transportation, the risk is a significant factor that should be considered due to the potential severe consequence of an incident. Regardless of risks, time is a paramount concern that should be considered in hazardous material transportation. In this way, this paper introduces a bi-objective model for a vehicle routing and scheduling problem of hazardous material distribution problems under the fuzzy condition to minimize both total distribution time and risks. In the proposed model, the fuzzy inference system and fuzzy failure mode and effects analysis are applied to identify and calculate the high-level risks instead of the previous simple methods for the first time. Moreover, Jimenez method and fuzzy goal programming are respectively utilized to convert the fuzzy bi-objective model into the same crisp and single-objective one. Besides, to cope with the NP-hardness of the large-sized problems, two meta-heuristic algorithms namely invasive weeds optimization and genetic algorithm is used, and several sensitivity analyses are performed to prove the efficiency of the proposed approach. The performance of the proposed algorithms is also assessed through a comparative study. Finally, the proposed model is implemented to a real case study to prove the validity of the model.

Joint decision on product greenness strategies and pricing in a dual-channel supply chain: A robust possibilistic approach

Nowadays, by increasing the sensitivity of governments to the issue of the environment, green supply chain design has become very significant. This paper presents a mathematical model based on a dual-channel system for designing a green supply chain. The first sales channel is based on the traditional retail purchasing system, while in the second channel the customers purchase the products from the plants directly. In the proposed model, decisions on location, products transfer and pricing are determined. Furthermore, the decision on the kind of manufacturing technology in each plant to be established is considered as well. Any kind of technology leads to a specific level of the greenness of the products in each plant. A product with a higher green level has a lower greenhouse gas emission but higher costs. In order to encourage managers to produce green products, government subsidy policies are in place. Besides, because of the uncertain nature of some parameters, a robust possibilistic optimization approach has been used. As the proposed mixed integer nonlinear model is complicated to solve, an Invasive Weed Optimization Algorithm (IWO) is proposed to obtain efficient solutions. Due to the nonlinear type of the developed model and due to there is no benchmark available in the related literature to validate the results, Cultural Algorithm (CA) and Genetic Algorithm (GA) are utilized. The results indicate that IWO performs better than others.

Job rotation scheduling in the Seru system: shake enforced invasive weed optimization approach

PurposeLine–cell conversion and rotation of operators between cells are common in lean production systems. Thus, the purpose of this study is to provide an integrated look at these two practices through integrating job rotation scheduling and line-cell conversion problems, as well as investigating the effect of rotation frequency on flow time of a Seru system.Design/methodology/approachFirst, a nonlinear integer programming model of job rotation scheduling problem and line–cell conversion problem (Seru-JRSP) was presented. Then, because Seru-JRSP is NP-hard, an efficient and effective invasive weed optimization (IWO) algorithm was developed. Exploration process of IWO was enhanced by enforcing two shake mechanisms.FindingsComputations of various sample problems showed shorter flow time and less number of assigned operators in a Seru system scheduled through job rotation. Also, nonlinear behavior of flow time versus number of rotation periods was shown. It was demonstrated that, setting number of rotation frequency to one in line with the literature leads to inferior flow time. In addition, ability of developed algorithm to generate clusters of equivalent solutions in terms of flow time was shown.Originality/valueIn this research, integration of job rotation scheduling and line–cell conversion problems was introduced, considering lack of an integrated look at these two practices in the literature. In addition, a new improved IWO equipped with shake enforcement was introduced.

A Hybrid Algorithm Based on Invasive Weed Optimization Algorithm and Grey Wolf Optimization Algorithm

In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the orig.

A self-adaptive exhaustive search optimization-based method for restoration of bridge defects images

Existing bridges are aging and deteriorating. Furthermore, large number of bridges exist in transportation networks meanwhile maintenance budgets are being squeezed. This state of affairs necessities the development of automatic bridge defects evaluation model using computer vision technologies to overcome the limitations of visual inspection. The digital images are prone to degradation by noises during the image acquisition phase. The absence of efficient bridge defects image restoration method results in inaccurate condition assessment models and unreliable bridge management systems. The present study introduces a self-adaptive two-tier method for detection of noises and restoration of bridge defects images. The first model adopts Elman neural network coupled with invasive weed optimization algorithm to identify the type of noise that corrupts images. In the second model, moth-flame optimization algorithm is utilized to design a hybrid image filtering protocol that involves an integration of spatial domain and frequency domain filters. The proposed detection model was assessed through comparisons with other machine learning models as per split validation and tenfold cross validation. It attained the highest classification accuracies, whereas the accuracy, sensitivity, specificity, precision, F-measure and Kappa coefficient are 95.28%, 95.24%, 98.07%, 95.25%, 95.34%. 95.43% and 0.935, respectively in the separate noise recognition module. The capabilities of the proposed restoration model were evaluated against some well-known good-performing optimization algorithms in addition to some conventional restoration models. Moth-flame optimization algorithm outperformed other restoration models, whereas peak signal to noise ratio, mean-squared error, normalized absolute error and image enhancement factor are 25.359, 176.319, 0.0585 and 7.182, respectively.

Optimal adaptive intuitionistic fuzzy logic control of anti-cancer drug delivery systems

This paper introduces a new closed loop fuzzy logic controller for regulating intravenous anti-cancer drug delivery, based on intuitionistic fuzzy sets and invasive weed optimization (IWO) algorithm. The developed intuitionistic fuzzy logic controller (IFLC) contributes the following advancements: First, the parameters of IFLC are adaptive and optimally tuned to achieve desired drug concentrations at the tumor site, killing almost all cancer cells at the end of treatment time. Second, drug delivery constraints; namely allowed levels of the drug dose and cumulative toxicity are naturally implied in the design of the optimized IFLC to ensure the clinical safety of cancer patients. Finally, the developed drug delivery control system is robust to handle all possible physiological conditions during treatment such as patient sensitivity in response to toxicity. To test and validate the performance of developed IFLC, extensive simulation results and comparative evaluation have been done on a mathematical patient model. It is proved that our optimal adaptive IFLC is superior to other methods in previous related studies, resulting in the best performance index and the minimum number of remaining cancer cells of 27.63 and 0.806, respectively.

A Suggestion Algorithm Instituted on Invasive Weed Optimization Algorithm and Bat Optimization Algorithm

In this paper, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents an Invasive Weed Optimization (IWO). This algorithm is a random numerical algorithm and the second algorithm repre- senting the Bat Optimization Algorithm (BA). This algorithm is one of the swarm intelligence algorithms in smart optimization. The Invasive Weed Optimization Algorithm is inspired by nature, as the weeds have colonial behavior and were introduced in 2006 by Mehrabian and Lucas. Invasive weeds pose a serious threat to crops due to their adaptability and pose a threat to the overall planting cycle. In the Invasive Weed Optimization Algorithm, the behavior of these weeds was analyzed and implemented. The Bat algorithm, which is called a swarm intelligence algorithm, was used to achieve the goal and the best solution. The algorithm was designed by Yang in 2010 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms Invasive Weed Optimization (IWO) and Bat Optimization Algorithm (BA) and we will symbolize the new algorithm IWO-BA Comparing the hybrid algorithm suggested with the original algorithms the results were excellent. In most research functions, the optimal solution has been found.

Tuning FPID Controller for an AVR System Using Invasive Weed Optimization Algorithm

In this paper, a design method to determine the optimal fractional proportional integral derivative (FPID) controller’s parameters is proposed for automated voltage regulator (AVR). The AVR model has been obtained utilizing two model reduction techniques, i.e., particle swarm optimization (PSO) and biogeography-based optimization (BBO) with the assistance of MATLAB and Simulink software package. For tuning the FPID controller, invasive weed optimization (IWO) algorithm is used with four unique performance indices: integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time weighted absolute error (ITAE). The results show that the IWO-designed FPID - compared to the classical proportional integral derivative controller (PID) - offers a better performance in terms of overshoot, rise time and settling time.

Estimation of Soil Temperature Based on Meteorological Parameters by the HYBRID INVASIVE Weed Optimization Algorithm Model

Soil temperature (ST) is considered as one of the crucial characteristics of soil affecting physical and chemical processes of soil, agricultural products, the optimal time for planting seeds, etc and land surface ecological system. Hence, estimating this parameter can play an important role in agricultural and hydrology engineering. In this study, soil temperature was estimated using multilayer perceptron (MLP) and hybrid models of multilayer perceptron with invasive weed optimization algorithm (MLP-IWO). For training and testing of intelligence models meteorological data such as air temperature, relative moisture, wind speed, sunshine hours with a ten-year statistical period during 2007-2017 related to the meteorological station of Van in Turkey were used. The results showed for nominated scenario that the MLP-IWO performed best with lowest root mean square error (RMSE) at 2.057 °C when compared with original MLP model with RMSE at 2.426 °C, the MLP-IWO model ranked first. With greatly reduced modeling error rate, the MLP-IWO is suggested as superior models in soil temperature modeling and may be extended to other aspect of soil science. Overall, new hybrid MLP-IWO methods showed the potential to estimate soil temperature from freely available meteorological data, which may help reduce costs and labour in the field and this model has provided as a high accurate model for estimation soil temperature.

Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics

The integration of big data analytics and cognitive computing results in a new model that can provide the utilization of the most complicated advances in industry and its relevant decision-making processes as well as resolving failures faced during big data analytics. In E-projects portfolio selection (EPPS) problem, big data-driven decision-making has a great importance in web development environments. EPPS problem deals with choosing a set of the best investment projects on social media such that maximum return with minimum risk is achieved. To optimize the EPPS problem on social media, this study aims to develop a hybrid fuzzy multi-objective optimization algorithm, named as NSGA-III-MOIWO encompassing the non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective invasive weed optimization (MOIWO) algorithms. The objectives are to simultaneously minimize variance, skewness and kurtosis as the risk measures and maximize the total expected return. To evaluate the performance of the proposed hybrid algorithm, the data derived from 125 active E-projects in an Iranian web development company are analyzed and employed over the period 2014-2018. Finally, the obtained experimental results provide the optimal policy based on the main limitations of the system and it is demonstrated that the NSGA-III-MOIWO outperforms the NSGA-III and MOIWO in finding efficient investment boundaries in EPPS problems. Finally, an efficient statistical-comparative analysis is performed to test the performance of NSGA-III-MOIWO against some well-known multi-objective algorithms.

An integrated optimization technique for optimal power flow solution

The aim of the research work is to propose an integrated optimization technique, established with the integration of the invasive weed optimization (IWO) and Powell’s pattern search (PPS) method. The IWO algorithm has been undertaken as a global search technique, which is inspired from the specific ecological behavior of weeds and has the ability to adapt to the changing environment. The local search PPS method is based upon a conjugate-based search and having excellent exploitation search capability, which helps to improve the solution obtained from IWO technique. The proposed technique is applied to solve optimal power flow (OPF) problem with the flexible AC transmission system devices. The OPF problem is a nonlinear, non-convex optimization problem and consists of continuous and discrete decision variables. The three objective functions comprise total fuel cost, pollutant emission and system transmission loss which are minimized sequentially. The proposed technique is tested on four standard IEEE test systems, and results are compared with the reported results in the literature and found promising. The results illustrate that the proposed technique performs better as compared to IWO technique in terms of the quality of solution and convergence characteristics. Further, t test is performed to validate the statistical performance of the proposed integrated optimization technique.

Variable selection in Poisson regression model using invasive weed optimization algorithm

Variable selection in Poisson regression model using invasive weed optimization algorithm

Variable selection in Poisson regression model using invasive weed optimization algorithm

Variable selection in Poisson regression model using invasive weed optimization algorithm

Application of metaheuristic control strategies to voltage regulation

This paper proposes four controllers applied to two existing generator models (1st and 4th order) with a type 1 excitation system taking into account nonlinearities. Jaya, crow search, and invasive weed optimization algorithm based PID controllers as well as adaptive neuro-fuzzy interface system controller were used to regulate the generator output in case of sudden voltage fluctuations. The results obtained were found to be very promising since most of them improved the uncompensated systems response in terms of overshoot, steady-state error, settling time and rise time. The overall best controller was found to be the Jaya based PID due to its responses as well as its ability to quickly converge to the best fitness.

A multi-level image thresholding approach using Otsu based on the improved invasive weed optimization algorithm

Multi-level thresholding is one of the most popular techniques in image segmentation. However, selecting the optimal thresholds with high accuracy and efficiency is still challenging. In this paper, a novel multi-level thresholding method using between-class variance (Otsu) based on an improved invasive weed optimization algorithm (FIWO) is proposed. In the FIWO algorithm, the forking technique of the lightning search algorithm is introduced to guarantee the quality of the initial population and to enhance the exploration of the algorithm. In addition, the current best solution swing operation is used to obtain the optimal thresholds with a fast convergence rate. Comparative experiments are carried out to test the performance of FIWO. The results show that the proposed FIWO algorithm is able to achieve better segmented images with fewer iterations than those of the simulated annealing algorithm, gravitational search algorithm, whale optimization algorithm and traditional invasive weed optimization algorithm.

An improved invasive weed optimization algorithm for solving dynamic economic dispatch problems with valve-point effects

ABSTRACT In this study, an improved invasive weed optimisation (CMIWO) algorithm is investigated to solve the dynamic economic dispatch (DED) problem with valve-point effects. In the proposed algorithm, a hybrid operator including selective crossover, random mutation and row crossover is proposed to improve the exploration and exploitation abilities. Moreover, a self-adaption repair method is developed and embedded into the proposed algorithm to repair infeasible solutions. To verify the optimisation performance of CMIWO, six well-known DED problems in three different-scale power systems are tested and compared with other algorithms that have been proposed in the literature. The experimental results show that CMIWO can find the more economical dispatch solutions compared to other algorithms, and the self-adaption repair method can successfully convert infeasible solutions into feasible solutions. The convergence ability of CMIWO is also verified after the detailed comparison.