Invasive Weed Optimization Algorithm Research Articles

Underwater TDOA/AOA joint localization algorithm based on hybrid invasive weed optimization algorithm

In order to achieve precise localization in the underwater environment, an underwater localization method based on hybrid invasive weed algorithm is proposed. First, considering the insufficient local search capability of the invasive weed algorithm, the salp swarm algorithm is used to pre-process the initial population, the position and velocity update formulas are employed to further improve the search depth, and the hybrid invasive weed algorithm is obtained. Then, the localization error is designed as the objective function to obtain the initial value of the source, and a new localization model is built using the initial values and interference parameters. Finally, an improved two-step weighted least square method is used to obtain the precise location of the source. The performance of the algorithm is also verified by comparisons with Cramer-Rao Lower Bound. Simulation experiments show that the proposed algorithm has better localization accuracy than other classic methods.

Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm

Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm

A Coverage Optimization Method for WSNs Based on the Improved Weed Algorithm.

Wireless sensor networks (WSNs) is a multi-hop wireless network composed of a group of static or mobile sensor nodes in the form of self-organization. Uneven distribution of nodes often leads to the problem of over coverage and incomplete coverage of monitoring areas. To solve this problem, this paper establishes a network coverage optimization model and proposes a coverage optimization method based on an improved hybrid strategy weed algorithm (LRDE_IWO). The improvement of the weed algorithm includes three steps. Firstly, the standard deviation of normal distribution based on the tangent function is used as the seed’s new step size in the seed diffusion stage to balance the ability of the global search and local search of weed algorithm. Secondly, to avoid the problem of premature convergence, a disturbance mechanism combining enhanced Levy flight and the adaptive random walk strategy is proposed in the process of seed breeding. Finally, in competition of invasive weed stage, the differential evolution strategy is introduced to optimize the competition operation process and speed up convergence. The improved weed algorithm is applied to coverage optimization of WSNs. The simulation results show that the coverage rate of LRDE_IWO is increased by about 1% to 6% compared with the original invade weed algorithm (IWO) and the differential evolution invasive weed optimization algorithm (DE_IWO), and the coverage rate of the LRDE_IWO algorithm is increased by 4.10%, 2.73% and 1.19%, respectively, compared with the antlion optimization algorithm (ALO), the fruit fly optimization algorithm (FOA) and the gauss mutation weed algorithm (IIWO). The results prove the superiority and validity of the improved weed algorithm for coverage optimization of wireless sensor networks.

An improved PTS scheme based on a novel discrete invasive weed optimization algorithm for PAPR reduction in the UFMC signal

An improved PTS scheme based on a novel discrete invasive weed optimization algorithm for PAPR reduction in the UFMC signal

New hybrid nature-based algorithm to integration support vector machine for prediction of soil cation exchange capacity

Soil cation exchange capacity (CEC) strongly influences the chemical, physical, and biological properties of soil. As the direct measurement of the CEC is difficult, costly, and time-consuming, the indirect estimation of CEC from chemical and physical parameters has been considered as an alternative method by researchers. Accordingly, in this study, a new hybrid model using a support vector machine (SVM), coupling with particle swarm optimization (PSO), and integrated invasive weed optimization (IWO) algorithm is developed for estimating the soil CEC. The physical and chemical data (i.e., clay, organic matter (OM), and pH) from two field sites of Taybad and Semnan in Iran were used for validating the new proposed approach. The ability of the proposed model (SVM-PSOIWO) was compared with the individual model (SVM) and the hybrid model (SVM-PSO). The results of the SVM-PSOIWO model were also compared with those of existing studies. Different performance evaluation criteria such as RMSE, R2, MAE, RRMSE, and MAPE, Box plots, and scatter diagrams were used to test the ability of the proposed models for estimation of the CEC values. The results showed that the SVM-PSOIWO model with the RMSE (R2) of 0.229 Cmol + kg−1 (0.924) was better than those of the SVM and SVM-PSO models with the RMSE (R2) of 0.335 Cmol + kg−1 (0.843) and 0.279 Cmol + kg−1 (0.888), respectively. Furthermore, the ability of the SVM-PSOIWO model compared with existing studies, which used the genetic expression programming, artificial neural network, and multivariate adaptive regression splines models. The results indicated that the SVM-PSOIWO model estimates the CEC more accurately than existing studies.

COVID-19 deterministic and stochastic modelling with optimized daily vaccinations in Saudi Arabia

In this paper, we investigate the stochastic nature of the COVID-19 temporal dynamics by generating a fractional-order dynamic model and a fractional-order-stochastic model. Initially, we considered the first and second vaccination doses as multiple vaccinations were initiated worldwide. The concerned models are then tested for the Saudi Arabia second virus wave, which is assumed to start on 1st March 2021. Four daily vaccination scenarios for the first and second dose are assumed for 100 days from the wave beginning. One of these scenarios is based on function optimization using the invasive weed optimization algorithm (IWO). After that, we numerically solve the established models using the fractional Euler method and the Euler-Murayama method. Finally, the obtained virus dynamics using the assumed scenarios and the real one started by the government are compared. The optimized scenario using the IWO effectively minimizes the predicted cumulative wave infections with a 4.4 % lower number of used vaccination doses.

A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak

As of March 24, 2020, the Food and Drug Administration (FDA) authorized to bleed the newly recovered from Coronavirus Disease 2019 (COVID-19), i.e., the ones whose lives were at risk, separate Plasma from their blood and inject it to COVID-19 patients. In many cases, as observed the plasma antibodies have cured the disease. Therefore, a four-echelon supply chain has been designed in this study to locate the blood collection centers, to find out how the collection centers are allocated to the temporary or permanent plasma-processing facilities, how the temporary facilities are allocated to the permanent ones, along with determining the allocation of the temporary and permanent facilities to hospitals. A simulation approach has been employed to investigate the structure of COVID-19 outbreak and to simulate the quantity of plasma demand. The proposed bi-objective model has been solved in small and medium scales using ɛ-constraint method, Strength Pareto Evolutionary Algorithm II (SPEA-II), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi Objective Invasive Weed Optimization algorithm (MOIWO) approaches. One of the novelties of this research is to study the system dynamic structure of COVID-19’s prevalence so that to estimate the required plasma level by simulation. Besides, this paper has focused on blood substitutability which is becoming increasingly important for timely access to blood. Due to shorter computational time and higher solution quality, MOIWO is selected to solve the proposed model for a large-scale case study in Iran. The achieved results indicated that as the plasma demand increases, the amount of total system costs and flow time rise, too. The proposed simulation model has also been able to calculate the required plasma demand with 95% confidence interval.

Wire length optimization of VLSI circuits using IWO algorithm and its hybrid

PurposeAdvancement in optimization of VLSI circuits involves reduction in chip size from micrometer to nanometer level as well as fabrication of a billions of transistors in a single die where global routing problem remains significant with a trade-off of power dissipation and interconnect delay. This paper aims to solve the increased complexity in VLSI chip by minimization of the wire length in VLSI circuits using a new approach based on nature-inspired meta-heuristic, invasive weed optimization (IWO). Further, this paper aims to achieve maximum circuit optimization using IWO hybridized with particle swarm optimization (PSO).Design/methodology/approachThis paper projects the complexities of global routing process of VLSI circuit design in mapping it with a well-known NP-complete problem, the minimum rectilinear Steiner tree (MRST) problem. IWO meta-heuristic algorithm is proposed to meet the MRST problem more efficiently and thereby reducing the overall wire-length of interconnected nodes. Further, the proposed approach is hybridized with PSO, and a comparative analysis is performed with geosteiner 5.0.1 and existing PSO technique over minimization, consistency and convergence against available benchmark.FindingsThis paper provides high performance–enhanced IWO algorithm, which keeps in generating low MRST value, thereby successful wire length reduction of VLSI circuits is significantly achieved as evident from the experimental results as compared to PSO algorithm and also generates value nearer to geosteiner 5.0.1 benchmark. Even with big VLSI instances, hybrid IWO with PSO establishes its robustness over achieving improved optimization of overall wire length of VLSI circuits.Practical implicationsThis paper includes implications in the areas of optimization of VLSI circuit design specifically in the arena of VLSI routing and the recent developments in routing optimization using meta-heuristic algorithms.Originality/valueThis paper fulfills an identified need to study optimization of VLSI circuits where minimization of overall interconnected wire length in global routing plays a significant role. Use of nature-based meta-heuristics in solving the global routing problem is projected to be an alternative approach other than conventional method.

Optimization of a conical cascade using invasive weed optimization (IWO) algorithm for multi-component systems: Investigation of the effect of feed flow on the separation factor of centrifuge machines in the cascade by DSMC method

One of the desirable properties that can be mentioned in the design of a cascade is to maximize the amount of separation work for the minimum number of machines so that the input feed does not exceed its maximum amount. For this purpose, the dilute gas area and part of the continuums area inside gas centrifuge are considered as the computational area for calculating the mass source. The mass source is calculated by direct simulation Monte-Carlo (DSMC) method. Based on the separation parameters obtained from the proposed flow function, the optimum cascade design was performed using the invasive weed optimization (IWO) algorithm. The results show that by considering the exact effect of feed by DSMC method in the Onsager equation, with increasing the separation parameters in a centrifuge machine, for a certain amount of separation in the product and waste streams in an optimal cascade, fewer centrifuge machines were obtained.

Design an Optimal Fractional Order PID Controller Based on New Algorithms and a Fuzzy Logic Controller to Regulate Type 1 Diabetes Patients

Diabetes is one of the most important diseases that researchers have focused on in scientific research since the time, because of the seriousness of this disease if it is not properly dealt with, especially with the emergence of some global epidemics such as Corona Virus (COVID 19), as the pancreas is the organ responsible for regulating sugar in the blood by secreting the insulin enzyme, insulin is widely used to control blood sugar. Therefore, it is important that the required insulin value is constant and controlled. The aim of this study is to control the blood glucose value that is achieved as a desired value and to maintain it as a constant value using a proportional, integral, and derivative control unit (FOPID) fractional order of the control parameters. In this research, the new control unit is applied to Bergman's mathematical model as a non-linear and simple model that simulates the mechanism of the interaction of glucose and insulin in the blood, and based on this, a closed control loop was designed to regulate the level of blood sugar to be an automatic control of blood glucose using the measured data from Special sensor. The contribution in this scientific paper is to define the (FOPID) parameters according to the closed loop responses of the system, and these parameters were adjusted using new meta-heuristic algorithms including the Invasive Weed Optimization (IWO), the PSO Particle Swarm optimization, the Genetic Algorithm (GA), The bat optimization algorithm (BA) and (ACO). As a result, the results of the five modern algorithms were compared based on several criteria to find out which one was better using MATLAB / SIMULINK simulation. It was found that the IWO algorithm performs better than PSO. The simulation results of the closed-loop system of this controller at the time of settling, overshoot and control inputs indicate very positive results compared to previous results. In addition, a new method has been proposed which is to design a pump in the form of a valve to control insulin pumping by controlling it with the fuzzy logic control unit, which in turn, we obtained better results, compared to the results of other previous studies.

Dispatch optimization of thermal power unit flexibility transformation under the deep peak shaving demand based on invasive weed optimization

The rapid development of new energy represented by wind power and photovoltaic power generation necessitates higher requirements for the flexibility of traditional thermal power generation units. The role of thermal power units will change from conventional main power to auxiliary power, not only because of the need for the survival and development of thermal power enterprises, but also because of the inevitable requirement for promoting the revolution of power energy production and consumption in China. The economy of thermal power unit flexibility transformation is an important constraint factor for decision-making in thermal power unit transformation. How to consider the economic factor of the thermal power unit transformation is a key problem for decision-making. In this paper, a nonlinear programming model is proposed to solve the problem. By introducing greedy strategy and improved reproduction strategy, an improved invasive weed optimization (IWO) algorithm is also proposed to solve the problem. Finally, a case study with three different scenario assumptions was conducted according to the most used transformation schemes of thermal power units in China, and the results were derived by using the proposed model and the improved IWO method. The comparative analysis of the results showed that the more the thermal power units participated in deep peak shaving, the greater the risk of the flexibility transformation of the thermal power units. In addition, under the condition of the same proportion of peak shaving, the risk of transformation without oil peak shaving was lower than that of oil peak shaving. Thus, the improved IWO algorithm can provide a stable and satisfactory solution. The research in this paper can provide a more scientific basis for decision-making on the flexibility transformation of the thermal power unit and can provide a reference for the selection of the flexibility transformation scheme of the thermal power unit.

Optimization of wear and frictional parameters using IWO and PSO algorithms

In the mechanical industries the tribological studies namely, wear rate (WR) and coefficient of friction (COF) are playing a significant role. Therefore, identifying the optimal parameters of wear and coefficient friction is a challenging task. To overcome this difficulty, in the present research work, the authors are using various non-traditional algorithms such as Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO) algorithms. The non-linear equation has been developed for T6-heat treated Al 7075/SiC/FA MMC’s using response surface methodology. The three independent factors such as sliding velocity (SV), applying load (AL), and sliding distance (SD) are used to optimize the WR and COF. Finally, the performances of the established algorithms are verified in terms of their capability to develop the optimum solution.

Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem

With the progress of technology, the multi-agent system is successfully applied in many applications. In this paper, we investigate the problem of multi-agent system reconnaissance mission scheduling, which is the core of the reconnaissance decision support system and can be modeled as an extension of Multi-Mode Multi-Skill Resource-Constrained Project Scheduling Problem. Three objectives are considered in this paper: (1) minimizing the reconnaissance mission’s makespan, (2) minimizing the total cost of allocating reconnaissance agents, and (3) maximizing the total quality of all reconnaissance tasks. An effective problem-specific multi-objective invasive weed optimization algorithm (PS-MOIWO) is proposed for solving the problem. Firstly, a new chromosome structure guaranteeing the feasibility of solutions and an initialization method are proposed. Secondly, we propose a self-adaptive penalty-based constraint handling technique to describe the fitness of each individual and adopt a novel non-dominated sorting method to rank the population. Thirdly, by using the problem-specific knowledge, a local search procedure is developed and incorporated into the PS-MOIWO framework to enhance the exploitation ability. Based on the Taguchi method, algorithm’s suitable parameter combinations are determined. Simulation results based on a set of newly generated reconnaissance instances and the comparisons with some existing algorithms demonstrate the proposed algorithm’s effectiveness.

Design and development of an adaptive-torque-based proportional-integral-derivative controller for a two-legged robot

The torque-based proportional-integral-derivative (PID) controller proposed in this paper is operated on joint motors by minimizing the error between the target and actual angular displacements of that joint to achieve a superior control. On the basis of the aforementioned phenomenon, the execution effectiveness of the two-legged robot’s gait can be controlled. The PID controller parameter tuning is a laborious and time-consuming process, and the controllers’ performance depends on the gain values set for the controller. To overcome the said drawbacks, this study develops an adaptive-torque-based PID controller for a two-legged robot that can provide adaptive gains on the basis of the magnitude of input signal received at the input nodes of the neural network (NN). Furthermore, the structure of the feedforward NN has been optimized with the help of the modified chaotic invasive weed optimization (MCIWO) algorithm. Moreover, the concept of zero moment point has been employed to check the balance of the two-legged robot while walking on various terrains (that is, flat, stair, and sloped surfaces). The effectiveness of the developed controller has been verified in both simulations and practical.

Modified IWO algorithm for topological optimum synthesis of the displacement amplifying compliant mechanism

The conventional mechanisms transmitted force and displacement through rigid members (high stiffness) and traditional joints (with high softness), where recently, researchers have come up with new systems called compliant mechanisms that transfer power and mobility through the deformation of their flexible members. One of the most frequently used approaches for designing compliant mechanisms is topology optimization. Extracting the optimal design of a displacement amplifying compliant mechanism using the modified Invasive Weed Optimization (MIWO) method is the current study's main novelty. The studied mechanism is a compliant micro-mechanism that can be used as a micrometric displacement amplifier. The goal of this synthesis is to maximize the output-to-input displacement ratio. In this research, a new random step is added to the Invasive Weed Optimization (IWO) method; the new seeds can be spread farther from their parents, which can be improved the algorithm's abilities. The results show that the use of the modified IWO algorithm for this problem has led to a significant improvement over the results from similar articles.

Research on Penetration Path Planning Method on Near Sea Bottom Based on the Sonar Detection Blind Zone

To improve the survivability and penetration probability of unmanned undersea vehicle(UUV) on near sea bottom combat missions, the factors leading to sonar detection blind zone are analyzed, and the detection blind zone produced by underwater terrain obscuration is mainly studied. A detection range calculation method based on DEM and acoustic ray is proposed, and the simulation study of the near sea bottom penetration path planning is carried out by using terrain blind zone and invasive weed optimization algorithm. Simulation results show the effectiveness of the proposed algorithm. This method can be applied to path planning of the UUV on near sea bottom penetration, which helps to improve Combat effectiveness and Operational effectiveness of UUV.

An Improved Partheno-Genetic Algorithm for Open Path Multi-Depot Multiple Traveling Salesmen Problem

The Multiple traveling salesmen problem (MTSP) is a complex combinatorial optimization problem, which is an extension of the well-known traveling salesmen problem (TSP). Compared to TSP, MTSP is more suitable to model real-life problems. In this paper, an open path multi-depot multiple traveling salesmen problem (OPMDMTSP) is studied. For the problem studied, two different objectives are considered: minimizing the total cost of all sales staff and minimizing the longest travel length. For the OPMDMTSP, an improved partheno-genetic algorithm (IPGA) is proposed in this paper. In IPGA, a new selection operator that combining roulette selection and elitist selection is implemented. In addition, a more comprehensive mutation operation that introduces the propagation mechanism of invasive weed optimization algorithm is used. Extensive experiment that compares the proposed method with some state of the art methods shows that the IPGA is outperform other methods in terms of both solution quality and convergence ability.

Simulation of crowd emergency evacuation based on the optimal algorithm of invasive weeds

This paper proposes an invasive weeds optimization algorithm to improve crowd emergency evacuation performance and applies it to crowd emergency evacuation simulation. The algorithm uses personnel status, safe places, and evacuation channels as emergency evacuation indicators to analyze the necessity of using intelligent optimization algorithms for emergency evacuation. Based on these data, this paper uses the invading weed optimization algorithm to model emergency evacuation. We regard the reciprocal of the crowd’s path as the fitness function and set the iterative threshold of the algorithm as the difference between the maximum and the minimum of the fitness. The paper aims to find the optimal solution to population emergency evacuation obtained by seed dispersal and weed competition. Experiments showed that, compared with the standard particle swarm optimization (PSO) and genetic algorithm (GA), the intrusion weed optimization algorithm has obvious advantages in the convergence speed and evacuation time of crowd emergency evacuation simulation.

Modeling and optimizing a multi-period closed-loop supply chain for pricing, warranty period, and quality management

Nowadays, most of the researchers have focused on collecting the used products to carry out the recovery process. This paper deals with the repair process to improve the virtual age of used products and integrate to forward flow as a closed-loop supply chain (CLSC). The products can be returned to the chain several times until they have the required quality to be repaired. Here the optimal number of returning and repairing the used products for maximization of the profits are calculated. Also, the price of selling the products, the acquisition cost, and the warranty period are determined to motivate the customers to bring back their used products and increase the demand for products. For our proposed multi-period problem, an appropriate inventory control policy is taken, and in case of increasing the production amount, additional capacity can be installed by extra cost. The proposed mixed-integer non-linear model has been solved by three metaheuristic algorithms: Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), Invasive Weeds Optimization algorithm (IWO). Numerical problems depicted model efficiency and by the use of the Taguchi method, qualitative parameters of proposed algorithms are calibrated. Then, the performance comparison of the methods has been done by Relative Performance Deviation.

Application of invasive weed optimization algorithm for optimizing the reloading pattern of a VVER-1000 reactor (in transient cycles)

In the present paper, for the first time, Invasive Weed Optimization (IWO) algorithm was utilized to optimize the reloading pattern of a nuclear reactor. This algorithm has been inspired by flexible, resistant and invasive behavior of weed in search for resources and appropriate location for growth and reproduction in the environment. The purpose of the optimization performed in this research was to achieve a reloading pattern with maximum effective multiplication factor along with safe maximum power peaking factor. Given that the diversity of loadable fuel assemblies was higher in the second cycle, as compared to other operational cycles, the optimization computations were conducted in this cycle to ensure high performance of the designed process under the most complicated conditions. In this respect, in order to assess fuel composition of the remaining assemblies from the first cycle and accurately calculate the objective parameters of the arrangements considered in the course of the optimization process, a program was developed wherein WIMSD5-B and CITATIO-LDI2 codes were utilized for neutronic simulations and WERL code was adopted for thermo-hydraulic modeling of the reactor core. Once finished with the optimization process, in an attempt to assure safety and optimality of the proposed arrangement during the operational cycle, the developed computational program was adopted to simulate time-dependent thermo-neutronic behavior of the reactor core during the second operational cycle. In addition, in order to ensure optimality of the proposed arrangement, firstly, it was compared to the arrangement proposed by the Russian contractor for a similar VVER-1000 reactor (Balakovo Nuclear Power Plant), and then, in another investigation, a comparison was made between the results of optimization using IWO algorithm and those using Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA). Results of all of the comparisons performed herein were indicative of superiority of the proposed optimization process in this research in terms of speed and comprehensiveness of the search process, and, more importantly, desirability of the proposed pattern.