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This study focuses primarily on sulfur dioxide (SO2) emissions control problem in a wet flue gas desulfurization (WFGD) process, and our objective is to design an intelligent control system so that the outlet SO2 concentration satisfies the SO2 emission standard. In our approach, a multimodel control framework, which is made up of a linear robust controller and a neural controller, is integrated with the invasive weed optimization (IWO) algorithm in an elegant fashion and used for SO2 emissions control purposes. A case study is carried out based on operation data from a 600 MW coal-fired unit, and simulation results show that IWO-based automatic clustering can identify different operating modes in the WFGD process with high accuracy. Further, the established multimodel control system can remove SO2 emissions effectively. Experimental results show that SO2 emissions can be removed effectively with the proposed method, and this could provide engineering guidance to design a WFGD control system. [ABSTRACT FROM AUTHOR]

Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup's recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we consider a power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) system, which is having I and Q (in-phase and quadrature) imbalance hardware impairment. The prime objective of this paper is to maximize the harvested energy for a SWIPT PS system, satisfying a minimum signal to noise ratio (SNR) requirement for information signal processing in the presence of IQ imbalance hardware impairment. The paper uses four types of bio-inspired algorithms like Particle Swarm Optimisation, Artificial Bee Colony, Firefly Algorithm and Invasive Weed Optimisation to attain the maximum harvested energy considering parameters of PS ratio, amplitude and phase imbalances in a Rayleigh fading environment. The simulation results show that under ideal power conversion efficiency and minimum hardware impairments, a maximum SNR of 16.7 dB and harvested energy of 11 dB is achieved for a transmit power of 20 dB in SWIPT systems for IWO bio-inspired algorithms in the 32 QAM modulation scheme. [ABSTRACT FROM AUTHOR]

Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup’s recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results.

One of the effectual text classification approaches for learning extensive information is incremental learning. The big issue that occurs is enhancing the accuracy, as the text is comprised of a large number of terms. In order to address this issue, a new incremental text classification approach is designed using the proposed hybrid optimization algorithm named the Henry Fuzzy Competitive Multi-verse Optimizer (HFCVO)-based Deep Maxout Network (DMN). Here, the optimal features are selected using Invasive Weed Tunicate Swarm Optimization (IWTSO), which is devised by integrating Invasive Weed Optimization (IWO) and the Tunicate Swarm Algorithm (TSA), respectively. The incremental text classification is effectively performed using the DMN, where the classifier is trained utilizing the HFCVO. Nevertheless, the developed HFCVO is derived by incorporating the features of Henry Gas Solubility Optimization (HGSO) and the Competitive Multi-verse Optimizer (CMVO) with fuzzy theory. The proposed HFCVO-based DNM achieved a maximum TPR of 0.968, a maximum TNR of 0.941, a low FNR of 0.032, a high precision of 0.954, and a high accuracy of 0.955. [ABSTRACT FROM AUTHOR]

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

In recent years, the number and severity of natural disasters have increased. These disasters always carry the risks of mortality and injury. But its mortality risks are much higher for people with disabilities than for normal people. One of the most important issues during natural disasters is to pay attention to the provision of accommodation and the possibility of evacuating people with disabilities. In this paper, a Mixed-Integer Linear Programming (MILP) model is proposed for shelter locations, evacuation routing, compatibility constraints in the context of disability, and time windows to picking up people with disabilities from different locations and transporting them to shelters. This study considers the time required for the movement of people with disabilities, heterogeneous vehicles, different capacities, several depots, and several shelters. The intended objective functions are the total distance traveled by vehicles, the maximum distance of a tour, and a hybrid objective function created by the weighted combination of both mentioned objective functions. A new Giza Pyramids Construction (GPC) algorithm is used to solve the model. Using the approach of the GPC algorithm, which is one of the soft computing methods, greatly reduces the computation complexity. To examine the performance of the proposed algorithm, thirty instances at different scales are generated with and without time window constraints. For validation, the results obtained from the proposed algorithm are compared with the results obtained from four algorithms including SA, IWO, DE, and EPC. The results show that the GPC algorithm performed better than other algorithms in solving the model. According to the results, the proposed GPC algorithm is 5%, 44%, 10%, and 4% better than SA, IWO, DE, and EPC, respectively. Furthermore, considering the time window significantly increases the total network evacuation time. Based on our experiments the proposed model and solution approach can be helpful in solving the real problems related to the evacuation of people with disabilities during natural disasters. [ABSTRACT FROM AUTHOR]

Summary: In emergency situations, the localization of Non Line of Sight (NLOS) nodes is essential for enhancing the reaction time of the vehicle drivers in response to the stimulus generated in VANETs. However, the existence of NLOS nodes intentionally or intentionally introduces broadcasting storm and congestion in the network. In this paper, a Hybrid Invasive Weed Optimization and Squirrel Search Algorithm‐Localization Mechanism (HIWO‐SSA‐LM) is proposed for improving the localization of NLOS nodes to enhance the reliable data dissemination under emergency situations in VANETs. This proposed HIWO‐SSA‐LM integrates the reproductive capability of Invasive Weeds into the reproductive potential of a Flying Squirrel Algorithm for sustaining the degree of balance under exploitation and exploration involved during NLOS node localization. The predominance of the proposed HIWO‐SSA‐LM is explored based on experiments conducted using EstiNet 8.1 simulator using emergency message delivery rate, channel utilization rate, and neighborhood awareness rate by varying the number of vehicles and NLOS nodes in the network. [ABSTRACT FROM AUTHOR]

One of the effectual text classification approaches for learning extensive information is incremental learning. The big issue that occurs is enhancing the accuracy, as the text is comprised of a large number of terms. In order to address this issue, a new incremental text classification approach is designed using the proposed hybrid optimization algorithm named the Henry Fuzzy Competitive Multi-verse Optimizer (HFCVO)-based Deep Maxout Network (DMN). Here, the optimal features are selected using Invasive Weed Tunicate Swarm Optimization (IWTSO), which is devised by integrating Invasive Weed Optimization (IWO) and the Tunicate Swarm Algorithm (TSA), respectively. The incremental text classification is effectively performed using the DMN, where the classifier is trained utilizing the HFCVO. Nevertheless, the developed HFCVO is derived by incorporating the features of Henry Gas Solubility Optimization (HGSO) and the Competitive Multi-verse Optimizer (CMVO) with fuzzy theory. The proposed HFCVO-based DNM achieved a maximum TPR of 0.968, a maximum TNR of 0.941, a low FNR of 0.032, a high precision of 0.954, and a high accuracy of 0.955.

Maximum likelihood (ML) method for direction of arrival (DOA) estimation achieves an excellent performance in array signal processing, but the complexity and computational load of searching the multidimensional nonlinear function prevented it from practical application. Based on squirrel search algorithm (SSA), an improved SSA (ISSA) for ML DOA estimation is proposed in this paper, which can reduces the computational complexity. The idea of spatial variation and diffuse inspired by the invasive weed optimization(IWO) algorithm is applied to ISSA. The simulation experiments compared ISSA with SSA, IWO, seeker optimization algorithm(SOA), sine cosine algorithm (SCA), genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE) method for ML DOA estimator show that the proposed algorithm has faster convergence speed, fewer iterations and lower root mean square error(RMSE) under different number of signal sources, different signal to noise ratio(SNR) and different population size. Therefor the proposed algorithm does not only ensure the estimation accuracy, but also greatly reduce the computation complexity of multidimensional nonlinear optimization for the ML method. Finally, the test experiment using Micro Electronic Mechanical Systems(MEMS) vector hydrophone array in Fenhe lake show the engineering practicability of proposed ML DOA estimator with ISSA.The results obtained will be valuable in the application of engineering.

Hybrid evolutionary methods combine approaches extracted from different evolutionary computation techniques to build a single optimization method. The design of such systems represents a current trend in the evolutionary optimization literature. In hybrid algorithms, the objective is to extend the potential advantages of the integrated approaches and eliminate their main drawbacks. In this work, a hybrid method for solving optimization problems is presented. The proposed approach combines (A) the explorative characteristics of the invasive weed optimization method, (B) the probabilistic models of the estimation distribution algorithms and (C) the dispersion capacities of a mixed Gaussian–Cauchy distribution to produce its own search strategy. With these mechanisms, the proposed method conducts an optimization strategy over search areas that deserve a special interest according to a probabilistic model and the fitness value of the existent solutions. In the proposed method, each individual of the population generates new elements around its own location, dispersed according to the mixed distribution. The number of new elements depends on the relative fitness value of the individual regarding the complete population. After this process, a group of promising solutions are selected from the set compound by the (a) new elements and the (b) original individuals. Based on the selected solutions, a probabilistic model is built from which a certain number of members (c) are sampled. Then, all the individuals of the sets (a), (b) and (c) are joined in a single group and ranked in terms of their fitness values. Finally, the best elements of the group are selected to replace the original population. This process is repeated until a termination criterion has been reached. To test the performance of our method, several comparisons to other well-known metaheuristic methods have been made. The comparison consists of analyzing the optimization results over different standard benchmark functions within a statistical framework. Conclusions based on the comparisons exhibit the accuracy, efficiency and robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

Nowadays, effort estimation in software projects is turned to one of the key concerns for project managers. In fact, accurately estimating of essential effort to produce and improve a software product is effective in software projects success or fail, which is considered as a vital factor. Lack of access to satisfying accuracy and little flexibility in existing estimation models have attracted the researchers’ attention to this area in last few years. One of the existing effort estimation methods is COCOMO (Constructive Cost Model) which has been taken importantly as an appropriate method for software projects. Although COCOMO has been invented some years ago, it has still got effort estimation ability in software projects. Many researchers have attempted to improve effort estimation ability in this model by improving COCOMO operation; but despite many efforts, COCOMO results are not satisfying yet. In this research, a new compound method is presented to increase COCOMO estimation accuracy. In the proposed method, much better factors are gained using combination of invasive weed optimization and COCOMO estimation method in contrast with basic COCOMO. With the best factors, the proposed model’s optimality will be maximized. In this method, a real data set is used for evaluating and its operation is analyzed in contrast to other models. Operational parameters improvement is affirmed by this model’s estimation results.

This paper presents a novel method for optimizing a planar elliptical dipole antenna with elliptical slots, for ultrawideband electromagnetic compatibility applications. The antenna is required to achieve minimum return loss and boresight realized gain with an adequate gain flatness across the frequency range of 1–5 GHz. Such an antenna is a powerful tool for electromagnetic measurements, due to its very compact size and its wide operating bandwidth. The main optimization method used in this paper is the invasive weed optimization (IWO), which is a nature-inspired metaheuristic evolutionary algorithm. The conventional and a modified version of IWO are compared to other prior-art evolutionary algorithms, such as the particle swarm optimization and differential evolution. The comparison is performed by applying all the methods on a set of mathematical test functions and also on the specific antenna design problem presented in this paper. The comparative results demonstrate the superiority of the modified IWO over the other optimization methods. [ABSTRACT FROM AUTHOR]

In this article, an efficient hybrid optimization algorithm based on invasive weed optimization algorithm and shuffled frog-leaping algorithm is utilized for optimum design of skeletal frame structures. The shuffled frog-leaping algorithm is a population-based cooperative search metaphor inspired by natural memetic, and the invasive weed optimization algorithm is an optimization method based on dynamic growth of weeds colony. In the proposed algorithm, shuffled frog-leaping algorithm works to find optimal solution region rapidly, and invasive weed optimization performs the global search. Different benchmark frame structures are optimized using the new hybrid algorithm. Three design examples are tested using the new method. This algorithm converges to better or at least the same solutions compared the utilized methods with a smaller number of analyses. The outcomes are compared to those obtained previously using other recently developed meta-heuristic optimization methods. [ABSTRACT FROM AUTHOR]

Heterogeneous wireless networks are an integration of two different networks. For better performance, connections are to be exchanged among the different networks using seamless Vertical Handoff. The evolutionary algorithm of invasive weed optimization algorithm popularly known as the IWO has been used in this paper, to solve the Vertical Handoff (VHO) and Horizontal Handoff (HHO) problems. This integer coded algorithm is based on the colonizing behavior of weed plants and has been developed to optimize the system load and reduce the battery power consumption of the Mobile Node (MN). Constraints such as Receiver Signal Strength (RSS), battery lifetime, mobility, load and so on are taken into account. Individual as well as a combination of a number of factors are considered during decision process to make it more effective. This paper brings out the novel method of IWO algorithm for decision making during Vertical Handoff. Therefore the proposed VHO decision making algorithm is compared with the existing SSF and OPTG methods.

Activated sludge models (ASMs) are often used in the simulation of the wastewater treatment process to evaluate whether the effluent quality parameters of a wastewater treatment plant meet the standards. The premise of successful simulation is to choose appropriate dynamic parameters for the model. A niche based adaptive invasive weed optimization (NAIWO) algorithm is proposed in this paper to find the appropriate kinetic parameters of activated sludge model 1 (ASM1). The niche idea is used to improve the possibility of convergence to the global optimal solution. In addition, the adaptive mechanism and periodic operator are introduced to improve the convergence speed and accuracy of the algorithm. Finally, NAIWO is used to optimize the parameters of ASM1. Comparison with other intelligent algorithms such as invasive weed optimization (IWO), genetic algorithm (GA), and bat algorithm (BA) showed the higher convergence accuracy and faster convergence speed of NAIWO. The results showed that the ASM1 model results agreed with measured data with smaller errors.

In this paper, hybridization of invasive weed optimization (IWO) and space transformation search (STS) are presented to solve, by applying multiple populations for multiple objectives individually, multiobjective optimization. This whole process is addressed as hybrid cooperative multiobjective optimization IWO (HCMOIWO). We carried out an application to solve system of nonlinear equations. In HCMOIWO, M single objectives are optimized simultaneously using the hybrid IWO with STS and all the nondominated solutions that are extracted from the group of parent weeds and offspring are stored in an archive, A . This archive is used not only to store nondominated solutions, but also to exchange information among subpopulations to explore the new search areas along the Pareto front. To exploit the nondominated solutions, a local search technique is adopted in HCMOIWO. The performance of HCMOIWO is evaluated with different sets of benchmark problems having different characteristics. Empirical results reveal the supremacy of HCMOIWO over state-of-the-art algorithms reported in recent literature. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to improve the performance of node localization in 3D space for wireless sensor network. To achieve this objective, we propose two range free localization algorithms for 3D space in anisotropic environment using the application of bacterial foraging optimization (BFO) and invasive weed optimization (IWO). In proposed methods, only received signal strength (RSS) information between nodes is sufficient for estimating target nodes locations. The RSS information gives clue to find out the distances between target nodes and anchor nodes. To overcome the non-linearity between RSS and distance, edge weights between target nodes and their neighbouring anchor nodes are considered to estimate the positions of target nodes. To further reduce the computational complexity and to model the edge weights, we use fuzzy logic system in this paper. BFO and IWO techniques are used to further optimize the edge weights separately to achieve the better localization accuracy. The simulation results show the superiority of the proposed algorithms as compared to centroid method, weighted centroid and existing 3D localization algorithms in terms of localization accuracy, stability, positioning coverage and scalability. [ABSTRACT FROM AUTHOR]

In this article, a wideband near-zero-index metamaterial (NZIM) unit cell, for gain improvement of a broadband microstrip fractal patch antenna is proposed and designed. The near-zero refractive index characteristics of metamaterial superstrate is used, which gathers the radiated waves from the source antenna and collimates them toward the normal direction. The reference fractal patch antenna operates at a relatively wide frequency range covering from 2.38 to 3.61 GHz, corresponding to a 40% fractional bandwidth. In order to effectively enhance the broadside gain of the antenna, a proposed unit cell is optimized using invasive weed optimization (IWO) algorithm to have the widest bandwidth at the required frequency band, in which the refractive index is less than unity (approaches zero). A prototype of the antenna was fabricated and tested. Measured results are in good agreement with the simulation. The measured −10 dB relative impedance bandwidth for the loaded antenna with NZIM is found to be 31%. The maximum measured broadside gain is about 3.1 dB higher than that of the reference antenna without NZIM, which corresponds to 86% aperture efficiency. [ABSTRACT FROM PUBLISHER]

This paper introduces a new, fast, efficient and global optimization algorithm called hybrid invasive weed optimization (IWO) and wind driven optimization (WDO). The hybrid algorithm is verified with six standard benchmark functions and implemented to the nulling pattern synthesis of a uniform linear array (ULA) and non-linear circular array (NUCA) antenna having a minimum side lobe level (SLL), and beam width to minimize the interference effect by optimizing the parameters of array elements. Two examples of ULA pattern synthesis with broad null at desired direction are considered to illustrate the efficacy of the proposed algorithm. The results are compared with those obtained by BEES, PGSA, BFO, WDO and MTACO algorithms. Further, one design example of NUCA antenna is considered and the results obtained by hybrid algorithm is compared with those obtained by other evolutionary algorithms such as GA, PSO, CS, FA, BBO, COA, and MIWO. Simulation studies demonstrate the improved performance of hybrid IWO/WDO algorithm in terms of synthesizing broad null, minimum SLL, beam width control and the rate of convergence for nulling pattern synthesis of ULA and NUCA antenna compared to other twelve reported algorithms. [ABSTRACT FROM AUTHOR]

An optimal synthesis of a wideband log-periodic dipole array (LPDA) is introduced in this paper. The LPDA optimization is performed under several requirements concerning the standing wave ratio, the forward gain, the gain flatness, the front-to-back ratio, and the sidelobe level, over a wide frequency range. The LPDA geometry that complies with the aforementioned requirements is suitable for efficient multimedia content delivery. The optimization process is accomplished by applying a recently introduced method called invasive weed optimization (IWO). The method has already been compared to other evolutionary methods and has shown superiority in solving complex nonlinear problems in telecommunications and electromagnetics. In this paper, the IWO method has been chosen to optimize an LPDA for operation in the frequency range of 800–3300 MHz. Due to its excellent performance, the LPDA can effectively be used for multimedia content reception over future mobile computing systems. [ABSTRACT FROM PUBLISHER]

In this paper, a new hybrid intelligent motion planning approach to mobile robot navigation is presented. In this new hybrid methodology, the invasive weed optimization (IWO) algorithm is used for training the premise parameters, and the least square estimation (LSE) method is used for training the consequent part of the adaptive neuro-fuzzy inference system (ANFIS). In this proposed navigational model, different kinds of sensor-extracted information, such as front obstacle distance (FOD), right obstacle distance (ROD), left obstacle distance (LOD), heading angle (HA), left wheel velocity (LWV), and right wheel velocity (RWV), are given input to the hybrid controller, in order to calculate the suitable steering angle (SA) for the robot. Using the IWO algorithm, the obtained root mean of squared error (RMSE) for the training data set in the ANFIS is 0.0013. The simulation results are verified by the real-time experimental results, using the Khepera III mobile robot to show the versatility and effectiveness of the proposed hybrid navigational algorithm. The results obtained using the proposed hybrid algorithm are validated by comparison with the results from other intelligent algorithms. Finally, it is proved that the proposed hybrid navigational controller can be implemented in the robot for navigation in any complex environments. [ABSTRACT FROM AUTHOR]

Heterogeneous wireless networks are an integration of two different networks. For better performance, connections are to be exchanged among the different networks using seamless Vertical Handoff. The evolutionary algorithm of invasive weed optimization algorithm popularly known as the IWO has been used in this paper, to solve the Vertical Handoff (VHO) and Horizontal Handoff (HHO) problems. This integer coded algorithm is based on the colonizing behavior of weed plants and has been developed to optimize the system load and reduce the battery power consumption of the Mobile Node (MN). Constraints such as Receiver Signal Strength (RSS), battery lifetime, mobility, load and so on are taken into account. Individual as well as a combination of a number of factors are considered during decision process to make it more effective. This paper brings out the novel method of IWO algorithm for decision making during Vertical Handoff. Therefore the proposed VHO decision making algorithm is compared with the existing SSF and OPTG methods. [ABSTRACT FROM AUTHOR]

This letter introduces a novel, fast, efficient, and global optimization algorithm called hybrid invasive weed optimization and wind-driven optimization (IWO/WDO). The proposed algorithm is implemented to synthesize the uniformly excited (an = 1, Øn = 0) linear sparse array pattern having a minimum sidelobe level (SLL), and null control with constraint on beamwidth to minimize the interference effect by optimizing the element position-only. Three examples of (N = 10, 28, and 32)-element linear array synthesis are considered to illustrate the efficacy of the proposed algorithm. The results are compared to those obtained by IWO, WDO, PSO, CLPSO, DE, and BBO algorithms. Simulation studies demonstrate that this algorithm achieves minimum SLL of -23.5, -13.22, and -19.7 dB as compared to -17.35, -10.5, and -15.70 dB obtained by the IWO algorithm for 10-, 28-, and 32-elements array, respectively. Also, it yields a null depth level (NDL) larger than -84.5 dB in all cases. The learning characteristics show a faster convergence at around 100 iterations. The simulation results demonstrate the improved performance of hybrid IWO/WDO algorithm compared to other six algorithms in terms of null control, minimum SLL, beamwidth control, and the rate of convergence. [ABSTRACT FROM PUBLISHER]

In this paper, the invasive weed optimization technique for compacting a microstrip elliptic low pass filter is proposed and validated. At the first step, the invasive weed optimization (IWO) technique is used to replace the shunt resonators transmission lines of the conventional filter with arbitrary n-segments Step Impedance Transmission Lines (SITLs) for compacting it. To validate the proposed method, the shunt resonator transmission lines of a seventh-order elliptic lowpass filter are replaced with proper SITLs which cause more than 30% compactness in this step. Then the IWO technique is applied on the general filter configuration to design a wideband harmonic suppression and compact elliptic low pass filter. This method extends the stop band bandwidth more than 8.5 f0 and also increases the compactness to 47%. The measurement results of the final optimized fabricated filter are in good agreement with the simulation ones. [ABSTRACT FROM AUTHOR]

Concerning absence of information exchange in basic invasive weed optimization, this paper proposed a new multi-agent invasive weed optimization algorithm by introducing mult-agent system. The modified competition & cooperation operator of mult-agent system was to boost the population' s communication and elevated the convergence speed. Besides this, it designed the self-study operator to improve the solution quality. The comparative experiments have been conducted on five benchmark test functions, to conclude that the multi-agent invasive weed optimization algorithm outperforms IWO, PSO and DE algorithm in convergence quality, speed and stability. [ABSTRACT FROM AUTHOR]

In order to overcome the drawbacks of the K-means (KM) for clustering problems such as excessively depending on the initial guess values and easily getting into local optimum, a clustering algorithm of invasive weed optimization (IWO) and KM based on the cloud model has been proposed in the paper. The so-called cloud model IWO (CMIWO) is adopted to direct the search of KM algorithm to ensure that the population has a definite evolution direction in the iterative process, thus improving the performance of CMIWO K-means (CMIWOKM) algorithm in terms of convergence speed, computing precision and algorithm robustness. The experimental results show that the proposed algorithm has such advantages as higher accuracy, faster constringency, and stronger stability. [ABSTRACT FROM AUTHOR]

Unit commitment (UC) is one of the most important aspect of power generation in the world today. Though, there is no method to find the exact optimized solution, there exists several meta-heuristic algorithms to determine the close to exact solution. This paper proposes a novel solution to effectively determine UC and generation cost using the technique of invasive weed optimization (IWO). The existing technique distributes the load demand among all the generating units. The method proposed here utilizes the output of UC obtained by using the Lagrangian relaxation (LR) method and calculates the required generation from only the plants that are ON discarding the OFF generator units and thereby giving a faster and more accurate response. Moreover, the results show the comparison between the LR-particle swarm optimization (PSO) and LR-IWO, and prove that the cost of generation for a 4 unit, 8 hour schedule is much less in the case of IWO when compared to PSO. [ABSTRACT FROM AUTHOR]

A new antenna array beamformer based on neural networks (NNs) is presented. The NN training is performed by using optimized data sets extracted by a novel invasive weed optimization (IWO) variant called modified adaptive dispersion IWO (MADIWO). The trained NN is utilized as an adaptive beamformer that makes a uniform linear antenna array steer the main lobe toward a desired signal, place respective nulls toward several interference signals, and suppress the side lobe level (SLL). Initially, the NN structure is selected by training several NNs of various structures using MADIWO-based data and by making a comparison among the NNs in terms of training performance. The selected NN structure is then used to construct an adaptive beamformer, which is compared to MADIWO-based and ADIWO-based beamformers, regarding the SLL and the ability to properly steer the main lobe and the nulls. The comparison is made, considering several sets of random cases with different numbers of interference signals and different power levels of additive zero-mean Gaussian noise. The comparative results exhibit the advantages of the proposed beamformer. [ABSTRACT FROM AUTHOR]

This communication introduces a hybrid invasive weed optimization and particle swarm optimization (IWO/PSO) algorithm dedicated to pattern synthesis of conformal arrays. A 3\, \times \,9 cylindrical conformal microstrip array is studied to demonstrate the proposed algorithm. The active element pattern of each element in the conformal array is extracted and the phase and amplitude of each excited voltage are optimized to achieve the required goals using the hybrid IWO/PSO algorithm. The results indicate that, compared with standard IWO and PSO algorithms, the introduced hybrid algorithm maintains the respective advantages of the IWO and PSO algorithms while excluding their respective deficiencies. Moreover, the hybrid algorithm is very reliable in achieving global optimality. [ABSTRACT FROM AUTHOR]

An ecologically inspired optimization algorithm, called invasive weed optimization (IWO), is presented for the design of non-uniform, planar, and circular antenna arrays that can achieve minimum side lobe levels for a specific first null beamwidth while avoiding the mutual coupling effects simultaneously. IWO recently emerged as a derivative-free real parameter optimizer that mimics the ecological behavior of colonizing weeds. For the present application, classical IWO has been modified by introducing a more explorative routine of changing the standard deviation of the seed population (equivalent to mutation step-size in evolutionary algorithms) of the algorithm. Simulation results over three significant instances of the circular array design problem have been presented to illustrate the effectiveness of the modified IWO algorithm. The design results obtained with modified IWO have been shown to comfortably beat those obtained with other state-of-the-art metaheuristics like genetic algorithm (GA), particle swarm optimization (PSO), original IWO and differential evolution (DE) in a statistically meaningful way. [ABSTRACT FROM AUTHOR]

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. • Various cost and benefit effects are considered of the thermal power unit's flexibility transformation. • An economic risk optimization model of the thermal power unit's flexibility transformation is constructed. • Invasive weed optimization (IWO) algorithm is proposed to solve the optimization model. • The greater the proportion of thermal power units participating in deep peak shaving, the more economic risk of the units. [ABSTRACT FROM AUTHOR]