Invasive Weed Optimization Algorithm Research Articles

Commercial wind turbines modeling using single and composite cumulative probability density functions

As wind turbines more widely used with newer manufactured types and larger electrical power scales, a brief mathematical modelling for these wind turbines operating power curves is needed for optimal site matching selections. In this paper, 24 commercial wind turbines with different ratings and different manufactures are modelled using single cumulative probability density functions modelling equations. A new mean of a composite cumulative probability density function is used for better modelling accuracy. Invasive weed optimization algorithm is used to estimate different models designing parameters. The best cumulative density function model for each wind turbine is reached through comparing the RMSE of each model. Results showed that Weibull-Gamma composite is the best modelling technique for 37.5% of the reached results.

Two-hybrid sine cosine algorithm based on Invasive weed optimization algorithm and Bat algorithm

This research paper takes two hybrids. First hybrid is a sine cosine algorithm with evolution algorithm invasive weed optimization (IWO) resulting that improved algorithm has highly properties that we so called SCA-IWO. Second hybrid is a sine cosine algorithm (SCA) with Bat algorithm optimization (BA). Which is considered one of the intelligent swarm algorithms related to intelligent arithmetic branches and have got on hybrid algorithm, so called SCA-BA. In these two improved algorithms that we have gained which have high properties that they can overcome over the problems that confront the SCA, IWO and BA, each separately. In addition to that, it can pass the falling in local solutions and slow converge, so they gained the global value in vast majority of numerical calculation. SCA-IWO and SCA-BA haven been applied on set of function by specialists in testing optimization that has high measurement with certain iterations. Numerical results proved the efficiencies of hybrids of SCA-IWO and SCA-BA with excellent results compared with original SCA, as well as getting a global and minimum value in many used functions.

Optimizing a two-level closed-loop supply chain under the vendor managed inventory contract and learning: Fibonacci, GA, IWO, MFO algorithms

This paper addresses a new multi-level serial closed-loop supply chain (CLSC) model with an integrated formulation of batch deliveries, quality-dependent return rate, random defective rate, rework process, and learning effects. The vendor managed inventory contract has motivated companies to cope with the bullwhip effect and its associated shortages. However, getting precise information on manufacturing/remanufacturing time is a challenge owing to human learning. Accordingly, this paper tries to incorporate the learning effect on the inventory control problem. This is a bridge to fill the gaps in the literature by considering how the learning process affects the production time and system costs in a CLSC problem. Moreover, the mean-standard deviation utility function is used in order to take the random defective rate into account, to minimize mean system costs with respect to the standard deviation. Due to the complexity of the proposed mixed-integer nonlinear programming model, three metaheuristic algorithms named, genetic algorithm, invasive weed optimization algorithm, and moth flame optimization algorithm are used to solve the problem, and Fibonacci algorithm will examine the validity of the applied methods. Two sets of numerical examples in small, medium, and large sizes are presented to illustrate the performance of the algorithms in terms of objective function values and required CPU-time. Afterward, a statistical analysis is utilized in order to evaluate the proposed algorithms. Finally, the results show how significantly the learning effect in manufacturing/remanufacturing time affects the costs of CLSC problem.

Gradient based invasive weed optimization algorithm for the training of deep neural network

Stacked Sparse Auto-Encoder (SSAE) is well known hierarchical deep neural networks for simulating the deep architecture of mammal brain. SSAE can be trained in a greedy layer-wise manner by using methods based on gradient such as Limited memory BFGS (LBFGS). However, methods based on gradient have many disadvantages. The main disadvantage is that they are sensitive to the initial value. In this paper, a meta-heuristic algorithm based on gradient, referred to GCIWOSS, is used to optimize the weights and biases of SSAE. Chaos strategy is firstly used to initial the population of IWO and then a new selection strategy is adopted with the purpose of improving the diversity of population and increasing the global exploration ability. The improved IWO is preparing for the following exploitation based on gradient to avoid falling into local optimal values. In the experiments, the proposed algorithm is proven to be effective in extracting features from different image datasets, compared with the LBFGS and several other feature learning models.

A novel hybrid IWO and CS algorithm for phase-only pattern synthesis of large array antenna

In this paper, a novel hybrid Invasive Weed Optimization (IWO) and Cuckoo Search (CS) algorithm (IWO/CS) is presented for phase-only pattern synthesis of large array antenna. The hybrid IWO/CS algorithm embeds the Levy flight of CS algorithm into IWO algorithm as a global guide, thereby enhancing the global search ability of the algorithm. At the same time, in order to avoid trapping into the local optimal solution, mutation operator is introduced to enhance search ability of the algorithm. Finally, competitive exclusion operation is improved to maintain the diversity of population. The hybrid algorithm is applied to phase-only beam shaping to achieve main lobe broadening and side lobe depression. Simulation results demonstrate the superiority of the hybrid IWO and CS algorithm.

Solution of capacitated vehicle routing problem with invasive weed and hybrid algorithms

The vehicle routing problem is widespread in terms of optimization, which is known as being NP-Hard. In this study, the vehicle routing problem with capacity constraints is solved using cost- and time-efficient metaheuristic methods: an invasive weed optimization algorithm, genetic algorithm, savings algorithm, and hybridized variants. These algorithms are tested using known problem sets in the literature. Twenty-four instances evaluate the performance of algorithms from P and five instances from the CMT data set group. The invasive weed algorithm and its hybrid variant with savings and genetic algorithms are used to determine the best methodology regarding time and cost values. The proposed hybrid approach has found optimal P group problem instances with a 2% difference from the best-known solution on average. Similarly, the CMT group problem is solved with about a 10% difference from the best-known solution on average. That the proposed hybrid solutions have a standard deviation of less than 2% on average from BKS indicates that these approaches are consistent.

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges face budgetary constraints. This state of affairs necessitates the development of a computer vision-based method to alleviate shortcomings in visual inspection-based methods. In this context, the present study proposes a three-tier method for the automated detection and recognition of bridge defects. In the first tier, singular value decomposition ([Formula: see text]) is adopted to formulate the feature vector set through mapping the most dominant spatial domain features in images. The second tier encompasses a hybridization of the Elman neural network ([Formula: see text]) and the invasive weed optimization (I[Formula: see text]) algorithm to enhance the prediction performance of the ENN. This is accomplished by designing a variable optimization mechanism that aims at searching for the optimum exploration–exploitation trade-off in the neural network. The third tier involves validation through comparisons against a set of conventional machine-learning and deep-learning models capitalizing on performance prediction and statistical significance tests. A computerized platform was programmed in C#.net to facilitate implementation by the users. It was found that the method developed outperformed other prediction models achieving overall accuracy, F-measure, Kappa coefficient, balanced accuracy, Matthews’s correlation coefficient, and area under curve of 0.955, 0.955, 0.914, 0.965, 0.937, and 0.904, respectively as per cross validation. It is expected that the method developed can improve the decision-making process in bridge management systems.

A Novel Industrial Image Contrast Enhancement Technique Based on an Improved Ant Lion Optimizer

This work introduces an improved ant lion optimizer (ALO), called BIALO, for industrial images. BIALO employs three strategies to improve the performance of the original ALO. First, a novel inertial weight is used to modify the ALO to better balance exploration and exploitation during the process of searching the best solutions. Second, the local search part of the bat algorithm plays an important role in accelerating the algorithm convergence rate. Additionally, the ALO is integrated with invasive weed optimization algorithm to further improve the searching precision. The proposed BIALO is applied to industrial image enhancement, where it acts as an efficient tool that searches for the best parameters in a local/global enhancement transformation. To test the performance of BIALO, we compare it with other metaheuristic algorithms, such as the genetic algorithm, particle swarm optimization, flower pollination algorithm, grasshopper optimization algorithm and the original ALO, on some benchmark industrial images. The experimental results establish that BIALO is able to achieve better outcomes than those of the other algorithms.

Array Pattern Synthesis of Multiband Hexagonal Fractal Antenna Array for Reduced Sidelobe and Deep Null Control using Modified Invasive Weed Optimization Algorithm

Array Pattern Synthesis of Multiband Hexagonal Fractal Antenna Array for Reduced Sidelobe and Deep Null Control using Modified Invasive Weed Optimization Algorithm

Invasive weed optimization based scheduling for digital microfluidic biochip operations

Digital Microfluidic Biochips (DMFBs) based on electro-wetting-on-dielectric (EWOD) technology are a class of lab-on-a-chip (LOC) devices. DMFBs can efficiently carry out biochemical analysis and have many advantages over the traditional laboratory system. DMFBs offer miniaturization, automation, and programmability. Resource-constrained scheduling is the first and vital step of fluidic-level synthesis of DMFBs while the other two are placement and routing of droplets. Scheduling DMFB operations is a constrained optimization problem which is NP-Complete. We propose an invasive weed optimization (IWO) algorithm based scheduling for the synthesis of DMFBs. The IWO algorithm is a nature-inspired meta-heuristic algorithm. Proposed algorithm can be used for the offline synthesis of DMFBs, where solution quality is more important than execution time. Each weed in the proposed algorithm represents a potential candidate solution for the scheduling problem. To calculate the fitness of individual weeds, we proposed an algorithm based on Heterogeneous Earliest Finish Time (HEFT), which incorporates resource binding, scheduling, and greedy module selection mechanism for bio-assay operations. Weeds (solutions) update their positions (priorities) by colonization behavior of weeds. Simulation results show that proposed IWO outperforms iterative improvement based algorithms and optimal ILP based algorithms which are existing for the offline synthesis of DMFBs.

Hybrid PSO-HSA and PSO-GA algorithm for 3D path planning in autonomous UAVs

Unmanned aerial vehicles (UAVs) are a quintessential example of automation in the field of avionics. UAVs provide a platform for performing a wide variety of tasks, but in each case the concept of path planning plays an integral role. It helps to generate a pathway free of obstacles, having minimum length leading to lesser fuel consumption, lesser traversal time and helps in steering the aircraft and its corresponding antenna power signature safely around the hostile antenna to avoid detection. To optimize path planning to incorporate all the above-mentioned constraints, this paper presents two new hybrid algorithms particle swarm optimization (PSO) with harmony search algorithm and PSO with genetic algorithm. The hybrid algorithms perform both an exploratory and exploitative search, unlike the existing algorithms which are biased, towards either an exploitative search or an exploratory search. Furthermore, the hybrid algorithms are compared to the existing optimization algorithms and in all cases the hybrid algorithms give a minimum of 7% better result against PSO with up to a 40% better result against Invasive Weed optimization algorithm for a fixed computational time, suggesting better real-time applications.

Invasive Weed Optimized Area Centralized 2 Degree of Freedom Combined PID Controller Scheme for Automatic Generation Control

Frequency disturbances caused by load perturbations of multi area interconnected power system are reduced by using proper control schemes provided via secondary control loop along with primary control of generation plants. These secondary controllers provide simultaneous control signal to individual machines of a particular area when the control scheme is area centralized. This area centralized mechanism provides quick control action with less computational burden since control problem dimensions gets reduced even the area consists multiple plants/machines. Such centralized new cascade control scheme is proposed in this paper to supervise the secondary control mechanism and it is implemented with parallel connection of 2-Degree of Freedom Proportional-Integral-Derivative (2-DOF PID) controller combined with regular PID controller. The performance of this new controller is studied on multi area multi machine interconnected power system with participation factor concept and later its relative performance in terms of dynamic and steady state specifications are compared with conventional PID and 2-DOF PID controllers. These control parameters are tuned by Invasive Weed Optimization (IWO) algorithm to achieve better system outputs. Case studies presented in this paper show the advantages of proposed control scheme.

Multi‐objective long‐term reconfiguration of autonomous microgrids through controlled mutation differential evolution algorithm

This study provides a solution for the feeder reconfiguration of autonomous microgrids (MGs). The objective is to minimise power loss, switching costs, and enhance voltage stability index, considering time-variations of loads. The daily load profiles for different seasons (spring, summer, fall, and winter) of different customers (i.e. residential, industrial, and commercial) are considered. In order to reduce the dimensions of the optimisation problem, k-means algorithm is implemented that clusters seasonal/yearly load profile into a few groups. The daily load profile is obtained based on the average of the group which has maximum members. This ensures the selection of a subset of load profiles that effectively represent the entire year's profile to reduce the complexity and execution time of the model. Subsequently, a new method is developed to break the daily load profile into intervals that guarantee less switching frequency for dynamic network reconfiguration. A controlled mutation differential evolution algorithm (CMDEA) compatible with long-term reconfiguration problem is developed with superior performance compared to conventional DEA and invasive weed optimisation algorithm. The CMDEA is employed to solve the reconfiguration problem on 33-bus and 69-bus autonomous MGs. Simulation results validate the effectiveness of the proposed method to reduce operational costs and computational burden in a smart grid environment.

Optimal Parameter Estimation in Activated Sludge Process Based Wastewater Treatment Practice

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.

Invasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit

Inasmuch as channels are designed to mitigate continues sedimentation, sediment transport models have been developed to calculate flow velocity to keep sediment particles in motion. In order to promote the computation capability of sediment transport models, recently machine learning algorithms have attracted interests, extensively. However, accuracy of such a model is attributed to the range of data and applied technique for model construction. For this purpose, the current study scrutinizes the applicability of “non-deposition with deposited bed” (NDB) concept for design of large channels applying hybrid machine learning algorithms. Through the modeling, firstly, conventional adaptive neuro-fuzzy inference system (ANFIS) technique is applied to develop a stand-alone model. In furtherance of improving the model’s performance, the ANFIS is hybridized with invasive weed optimization (IWO) algorithm to construct a hybrid ANFIS-IWO model. As a benchmark, the ANFIS is further hybridized with classical genetic algorithm (GA) to compare with ANFIS-IWO outcomes. Furthermore, the developed machine learning models are compared to multigene genetic programming (MGP) and particle swarm optimization (PSO) stand-alone machine learning results reported in the literature and classical regression models by means of variety of statistical performance measurements. Hybridization of ANFIS with IWO, enhances its accuracy with a factor of 30%. Respecting to the models performance examination, the ANFIS-IWO model is found superior to its alternatives for sediment transport computation. The thickness of the deposited bed and deposited bed width are found as effective parameters for sediment transport modeling in open channels with a bed deposit.

IAM: an improved mapping on a 2-D network on chip to reduce communication cost and energy consumption

Based on the recent research, the communication cost has been the most important open issue in network on chip (NoC). In other words, the lower the communication cost, the better the performance of the NoC and the lower the energy consumption. In this regard, although different mapping algorithms are proposed, they could not efficiently address some important challenges such as high complexity, early convergence at the local optimum, and performing well for all task graphs. The proposed method named IAM (IWO algorithm mapping) is an enhanced 2D-mesh-based-NoC mapping method which adapts the invasive weed optimization (IWO) algorithm, in order to efficiently map the IP cores to routers. The obtained results indicate that the communication cost improved 13, 9, 8, 4, and 4 percent in comparison with the LMAP, the CASTNET, the CLUSTER, the NMAP, and the PSO algorithm, respectively. Regarding energy consumption, IAM outperforms the NMAP, the CASTNET, and the CMAP and the Onyx algorithms by providing 15, 10, 7, and 7 percent improvement in energy consumption, respectively. Max delay was reduced by 11, 4, 5, and 5 percent compared to NMAP, CASTNET, CMAP, and Onyx algorithms, respectively. Throughput was improved by 9, 9, 4, and 10 percent compared to NMAP, CASTNET, CMAP, and Onyx algorithms, respectively.

A novel self-tuning type-2 fuzzy maximum power point tracking technique for efficiency enhancement of fuel cell based battery chargers

Given the uncertainties associated with proton-exchange membrane fuel cell systems and relatively low efficiency of the fuel cell stacks for low-power applications, designing a high-efficiency maximum power point tracking (MPPT) controller for the fuel cell electric vehicles is an important and also technically challenging issue. For this purpose, in this article, aiming to develop a high-efficiency and low cost battery charger, a novel self-tuning type-2 fuzzy MPPT controller is presented. The main task of the controller is to provide the better performance and regulate the switching duty cycle of the used power converter under the system's uncertainty conditions in order to dynamically extract the maximum power from the fuel cell system and maintain the battery at its highest possible state of charge while protecting it from overcharging. For the sake of computational efficiency, an improved invasive weed optimization algorithm, called elitist invasive weed optimization (EIWO), is also presented to tune the type-2 fuzzy set parameters, whose improvement is demanding due to the limited human experience and knowledge. All data processing and simulations are conducted in the MATLAB software. Finally, the performance of the proposed MPPT controller is examined through using experimental tests with a prototype device.

Stochastic approach for channel selection in cognitive radio networks using optimization techniques

Secondary Users (SU) are guided by Cognitive Radio Device in identifying a channel licensed to Primary Users (PU) when it is free. Whenever PU arrives, SU shall vacate and search for the alternate band by a conditional handoff. Existing handoff methods reduce the network’s efficiency. The proposed stochastic approach under hybrid Spectrum Hand-off is (SHO) employing the Invasive Weed Optimization (IWO) algorithm thus increasing the SHO efficiency in CR Networks. The proposed Centralized Cognitive Device monitors load balancing, minimizes handoff delay as well as the SU’s service time, conserves energy, speeds up the data transmission, supports the Internet of Things, multimedia applications including audio, live video streaming and still images over resource constrained WSNs. This algorithm is compared with the existing Genetic algorithm and Particle Swarm Optimization. The channel selection accuracy of the proposed IWO method is found to be 97.8% and it outperforms conventional methods. Besides, the pre-emptive resume priority M/G/1 queuing model is also utilized. This paper presents a complete system model along with a detailed study of its parameters proving the technique’s effectiveness.

A two-layer improved invasive weed optimization algorithm for optimal operation of cascade reservoirs

Abstract This paper proposes a two-layer improved invasive weed optimization (TIIWO) algorithm to overcome the disadvantages of the low quality of its initial population and the low optimization performance of IWO. The TIIWO algorithm includes dynamic corridor constraints (in its outer layer) and iterative reciprocating optimization (in its inner layer). The convergence of the TIIWO algorithm is achieved by minimizing the Schaffer function, which is characterized by its strong oscillatory behavior. In addition, the sensitivity of the main TIIWO parameters is analyzed using two methods, namely the revised Morris scheme and the Sobol index method. For experimental assessment, the TIIWO algorithm is firstly applied to a single reservoir. We investigate how the algorithm convergence is affected by four algorithm variants and parameter values. Then, the TIIWO algorithm is used to solve the problem of the optimal operation of cascade reservoirs. The results show that the TIIWO algorithm quickly and efficiently reaches the optimal operation of cascade reservoirs. In addition, this algorithm exhibits superior performance for high-dimensional, nonlinear and multi-constraint problems.

Invasive Weed Optimization Algorithm for Tuning Transitioning from Type-1 to Interval Type-2 Fuzzy Logic Controller for Boost DC-DC Converters

Invasive Weed Optimization Algorithm for Tuning Transitioning from Type-1 to Interval Type-2 Fuzzy Logic Controller for Boost DC-DC Converters