Invasive Weed Optimization Technique Research Articles

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

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

Improved power quality operation of symmetrical and asymmetrical multilevel inverter using invasive weed optimization technique

Low switching frequency pulse width modulation (PWM) technique for modulation and control of multilevel inverter in medium voltage high power applications is preferred in order to reduce the switching losses. In this context, a multilevel inverter operated with Selective harmonics minimization PWM technique offers better quality waveform at reduced switching losses. After the Fourier series analysis, the system of non-linear simultaneous transcendental equations is obtained. These equations are then solved to obtain switching angles to have certain low order harmonics at minimum value and regulation in the fundamental voltage magnitude. In this paper, a novel invasive weed optimization (IWO) technique is proposed to compute switching angles. The proposed technique can compute switching angles for both symmetrical and asymmetrical multilevel inverters. Thus it has superiority over well-known optimization techniques such as GA, PSO, DE, and ACO, etc. Moreover, in certain modulation index ranges, it provides faster convergence and accurate results which have been demonstrated in the paper. The computational results have been verified with the experimental result on the prototype developed in the laboratory. The field programming gate arrays (FPGA) based controller is used to implement the proposed technique. The hardware results have been found in close agreement with the computed results.

An Invasive Weed Optimization for Sensor Less Control of Grid Integrated Wind Driven Doubly Fed Induction Generator

The article presents the design and development of invasive weed optimization (IWO) for the sensor less speed control of doubly fed induction generator (DFIG) under balanced and unbalanced conditions. A healthy condition represents the balanced condition while unbalancing condition is characterized by unhealthy conditions like three-phase fault, single line to ground fault, the line-to-line fault, and double line to ground fault. The DFIG is driven by wind and integrated with the grid. The advantages associated with IWO technique are a simple mathematical approach and less data computation. Normally, DFIG consists of two back-to-back converters namely grid side converter (GSC) and rotor side converter (RSC). The GSC is an uncontrolled converter while the RSC is a controlled converter. The existing methods have poor performance parameters like settling time, peak overshoot for balanced conditions, and poor power quality parameters like total harmonic distortion (THD) for unbalanced conditions. An IWO technique has been applied to overcome such limitations. The effectiveness of the sensor less speed control is also analyzed with other techniques like Adaptive Neuro-Fuzzy Interference System (AN-FIS) & artificial neural network (ANN). The design of ANN is based on the feed-forward method using back propagation delay and the design of ANFIS is based on adaptive control and state space control strategy. It is observed that performance parameters like peak overshoot and settling time for the sensor less speed of DFIG are found to be more profound with IWO in comparison to ANFIS, ANN, and other existing techniques for balanced conditions. Similarly in the unbalanced condition, faulty current approaches are quite closer to their healthy state with the IWO method in comparison to other methods. In addition to this, minimum distortion (%THD) for the grid current under unbalanced conditions is also attained with IWO in comparison to ANFIS, ANN, and other existing techniques. Such application of IWO makes the system highly efficient and robust.

Adaptive partitioning‐based copy‐move image forgery detection using optimal enabled deep neuro‐fuzzy network

AbstractThe emergence of photo editing applications, like Adobe Photoshop, has manipulated the operation of digital images into a simple task. However, these manipulations of images misrepresent the content of the original image for misleading the public. Various copy move forgery detection techniques are developed, but these show less robustness on the image with noise and blurring. This article develops an optimization‐driven deep learning technique for image forgery detection. The purpose is to develop a copy‐move image forgery detection technique using a deep neuro‐fuzzy network and a newly developed optimization algorithm. Here, adaptive partitioning is adapted using a rectangular search for splitting the image into different parts. In addition, the features like local Gabor XOR pattern and Texton features are extracted from the partition. Furthermore, the forgery is detected using the deep neuro‐fuzzy network. Finally, the deep neuro‐fuzzy network training is performed using the proposed multi‐verse invasive weed optimization (MVIWO) technique. The proposed MVIWO method will be newly designed by integrating the multi‐verse optimizer and invasive weed optimization technique. Thus, the copy‐move image forgery detection is effectively performed using the proposed MVIWO‐based deep neuro‐fuzzy network. The developed MVIWO‐based deep neuro‐fuzzy network offers superior performance with the highest specificity of 93.54%, highest accuracy of 94.01%, and highest sensitivity of 97.75%.

Improved Power Quality Operation of Symmetrical and Asymmetrical Multilevelinverter Using Invasive Weed Optimization Technique

Improved Power Quality Operation of Symmetrical and Asymmetrical Multilevelinverter Using Invasive Weed Optimization Technique

An integrated optimization technique for optimal power flow solution

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

Resolving of optimal fractional PID controller for DC motor drive based on anti-windup by invasive weed optimization technique

This paper exhibits a design procedure for tuning the parameters of Fractional Order Proportional Integral Derivative (FOPID) P controller to optimize the DC motor drive operation. The optimization technique is establishing on Invasive Weed Optimization (IWO). This paper also proposes the use of anti-windup aspect to against the saturation which may occur in the FOPID controller. The objective of this design is to improve the performance of the drive subjected to different transient response and loading conditions. A comparative study is carried out with a classical PID controller. The Matlab simulation results show more improvements in the proposed system.

A hybrid weed optimized coverage path planning technique for autonomous harvesting in cashew orchards

A coverage path planning algorithm is proposed for discrete harvesting in cashew orchards. The main challenge in such an orchard is the collection of fruits and nuts lying on the floor. The manual collection of fruits and nuts is both time consuming and labour intensive. The scenario begs for automated collection of fruits and nuts. There are methods developed in research for continuous crop fields, but none for discrete coverage. The problem is visualized as a graph traversal problem and paths for autonomous maneuvering are generated. A novel Mahalanobis distance based partitioning approach for performing coverage is introduced. The proposed path planner was able to achieve a mean coverage of 52.78 percentage with a deviation of 18.95 percentage between the best and worst solutions. Optimization of the generated paths is achieved through a combination of local and global search techniques. This was implemented by combining a discrete invasive weed optimization technique with an improved 2-Opt operator. A case study is formulated for the fruit picking operations in the orchards of Puducherry. The performance of the proposed algorithm is benchmarked against existing methods and also with performance metrics such as convergence rate, convergence diversity and deviation ratio. The convergence rate was observed to be 99.97 percent and 97.83 percent for a dataset with 48 and 442 nodes respectively. The deviation ratio was 0.02 percent and 2.16 percent, with a convergence diversity of 1.18 percent and 30.14 percent for datasets with 48 and 442 nodes. The achieved solutions was on par with the global best solutions achieved so far for the test datasets.

Application of improved invasive weed optimization technique for optimally setting directional overcurrent relays in power systems

This paper aims to study the application of a heuristic optimization technique namely, Invasive Weed Optimization (IWO) technique for optimal protection coordination in power systems. The optimal relay coordination problem is formulated as a nonlinear constrained optimization, which is solved using Improved IWO (IIWO). The proposed IIWO algorithm modifies the standard deviation expression of the weed population. The simulation results show that IIWO has faster and better convergence compared with standard IWO. To further improve the computational efficiency, a hybrid IIWO method is also proposed which is obtained by defining sequential quadratic programming (SQP) as a subroutine in IIWO for searching local solutions, thus eliminate weaker weeds in the colonization process. The proposed techniques are tested on both the 9-bus test system and IEEE- 30 bus systems and the performance is compared. Relay coordination algorithm is developed in MATLAB, and the results are found to be effective and reliable.

Optimal design of fractional order PID controller for time-delay systems: an IWLQR technique

ABSTRACTIn this paper, an optimal design based state feedback gain of fractional order proportional integral derivative (PID) controller for time delay system is proposed. The proposed optimal design is called as IWLQR, which will be the joined execution of both the invasive weed optimization (IWO) and linear quadratic regulator (LQR). The proposed technique modifies a fractional order proportional integral derivative (FOPID) regulator among a high order time delay scheme that achieves an elevated performance for a wide area. In the proposed methodology, the gain of the FOPID controller is tuned to achieve the desired responses which are determined using the LQR theory and the weight matrices of the LQR is anticipated with the assistance of IWO technique. The uniqueness of the projected technique is to reduce the fault in a PID regulator among the higher order time delay scheme by the aid of the increase limits of the regulator. The objective of the proposed control method is chosen in view of the set point parameters and the accomplished parameters from the time delay system. The projected method is employed to achieve the avoidance of high order time delay and the dependability restrictions such as tiny overrun, resolving time and fixed condition defect. This technique is carried out in MATLAB/Simulink platform and the results are separated by the earlier regulator junction representation like Z-N system, Wang technique, curve fitting technique, regression technique which illustrates the superior presentation of the anticipated abstaining in the existing work.

Hybridizing Invasive Weed Optimization with Firefly Algorithm for Multi-Robot Motion Planning

Path planning can be described as a NP-complete problem having various practical applications, as no polynomial time algorithm is capable of solving the problem available now. Autonomous mobile robot motion planning is one of these applications. In recent years, many researchers have developed algorithms inspired from the biological behaviour of different creatures. These techniques prove to be more efficient and robust. In this work, we have hybridized an evolutionary population-based algorithm invasive weed optimization technique which is derived from the invasive behaviour of weeds along with a swarm optimization algorithm, called firefly algorithm. Firefly optimization has been embedded into the existing invasive weed optimization to improve the position of the current weed population in the colony. The aim of hybridization is to avoid premature convergence in the large problem space and to maintain good balance between exploration and exploitation. The proposed hybridization IWFO maintains the efficient balance between exploration and exploitation because of adopting spatial dispersion property of IWO algorithm and movement property of the firefly, to explore new feasible region and exploits the new population of the weed colony. We have applied this hybrid algorithm for the motion planning of multi-robot in static environment. Computer simulation results prove the hybrid algorithm outperforms the traditional IWO Algorithm and firefly algorithm and overcomes the deficiencies of the individual algorithms.

The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint

Article history: Received June 25, 2015 Received in revised format: October 12, 2015 Accepted December 15, 2015 Available online December 17 2015 This paper presents a mathematical model to solve a multi-objective decision making supplier selection problem. The proposed problem considers three objective functions: the first objective function minimizes the cost of purchasing the products while the second objective function minimizes the due dates and finally the third objective function maximizes the customer satisfaction. The resulted problem is formulated as mixed integer programming and, therefore, we use invasive weed optimization technique to solve the resulted problem. The performance of the proposed model is compared with NSGA II based on different criteria such as mean ideal distance and quality matrix. The preliminary results indicate that the proposed model performs relatively well compared with alternative method. Growing Science Ltd. All rights reserved. 6 © 201

VARIANCE-BASED HARMONY SEARCH ALGORITHM FOR UNIMODAL AND MULTIMODAL OPTIMIZATION PROBLEMS WITH APPLICATION TO CLUSTERING

This article presents a novel variance-based harmony search algorithm (VHS) for solving optimization problems. VHS incorporates the concepts borrowed from the invasive weed optimization technique to improve the performance of the harmony search algorithm (HS). This eliminates the main problem of constant parameter setting in the algorithm proposed recently and named as explorative HS. It uses the variance of a current population as well as presents a solution vector to improvise the harmony memory. In addition, the dynamic pitch adjustment operator is used to avoid solution oscillation. The proposed algorithm is evaluated on 14 standard benchmark functions of various characteristics. The performance of the proposed algorithm is investigated and compared with classical HS, an improved version of HS, the global best HS, self-adaptive HS, explorative HS, and the recently proposed state-of-art gravitational search algorithm. Experimental results reveal that the proposed algorithm outperforms the above-mentioned approaches. The effects of scalability, noise, harmony memory size, and harmony memory consideration rate have also been investigated with the proposed algorithm. The proposed algorithm is then employed for a data clustering problem. Four real-life datasets selected from the UCI machine learning repository have been used. The results indicate that the VHS-based clustering outperforms the existing well-known clustering algorithms.