Modified Flower Pollination Algorithm Research Articles

Retraction Note: Modified flower pollination algorithm for optimizing FOPID controller and its application with the programmable n-level inverter using fuzzy logic

Retraction Note: Modified flower pollination algorithm for optimizing FOPID controller and its application with the programmable n-level inverter using fuzzy logic

Evolution inspired binary flower pollination for the uncapacitated facility location problem

The present paper introduces a modified flower pollination algorithm (FPA) enhanced by evolutionary operators to solve the uncapacitated facility location problem (UFLP), which is one of the well-known location science problems. The aim in UFLP is to select some locations to open facilities among a certain number of candidate locations so as to minimize the total cost, which is the sum of facility opening costs and transportation costs. Since UFLP is a binary optimization problem, FPA, which is introduced to solve real-valued optimization problems, is redesigned to be able to conduct search in binary domains. This constitutes one of the contributions of the present study. In this context, some evolutionary operators such as crossover and mutation are adopted by the proposed FPA. Next, the mutation operator is further enhanced by making use of an adaptive procedure that introduces greater level of diversity at earlier iterations and encourages intensification toward the end of search. Thus, while premature convergence and local optima problems at earlier iterations are avoided, a more intensified search around the found promising regions is performed. Secondarily, as demonstrated in this study, by making use of the reported evolutionary procedures, FPA is able to run in binary spaces without employing any additional auxiliary procedures such as transfer functions. All available benchmarking instances are solved by the proposed approach. As demonstrated by the comprehensive experimental study that includes statistically verified results, the developed approach is found as a promising algorithm that can be extended to numerous binary optimization problems.

Enhanced Task Scheduling and Resource Allocation in Edge-Cloud continuum Using Modified Flower Pollination Algorithm

Enhanced Task Scheduling and Resource Allocation in Edge-Cloud continuum Using Modified Flower Pollination Algorithm

Observer controller-based structure for a modified flower pollination algorithm for wind power generation

Observer controller-based structure for a modified flower pollination algorithm for wind power generation

State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network

Lithium-ion batteries (LIBs) need to maintain high energy efficiency and power level in several application scenario. Accurate state of health (SOH) forecast is essential for designing a safe and reliable battery management systems (BMS). Temporal convolutional network (TCN) is a prevailing deep learning method for estimating the SOH of lithium-ion batteries. However, the hyperparameters in the network are usually difficult to predefine, which poses a challenge for the SOH estimation accuracy in real-world. To solve this problem, this paper proposes a data-driven estimation approach, where the TCN is combined with the modified flower pollination algorithm (MFPA) to determine the network topology. After hyperparameter optimization, the external sensor raw data and identified ohmic resistances trajectories in the equivalent circuits model (ECM) are both input to the TCN model to estimate SOH of LIBs. In contrast to prior approaches for feature extraction, this method is not only conductive to improve SOH estimation accuracy, but also can reduce on-board estimation computing burden. We carry out experiments on the same type of cells from NASA public data resources. The experimental results systematically validate the superiority of the proposed method, which covers high estimation accuracy, great robustness to varied training set and satisfied universality to different batteries.

Particle-Based Optimization Algorithms for Longitudinal Control of Autonomous Vehicle: A Comparative Study

In order to improve the stability and performance of an autonomous vehicle, optimization needs to be explicitly performed in the controllers, which has an essential part in the tracking system. This work proposes a novel longitudinal control optimization scheme and a novel longitudinal controller consisting of a feed-forward and feedback term. The feed-forward term is inspired by the vehicle’s steady-state response, whereas the feedback term is a proportional-integral-derivative (PID) controller. Also, a model representing the longitudinal vehicle dynamics is designed based on physical phenomena affecting the vehicle. Besides, some nature-inspired optimization algorithms are used to determine the optimal model parameters and optimize the controller parameters, i.e., Particle Swarm Optimization (PSO), Accelerated PSO (APSO), Flower Pollination Algorithm (FPA), and Modified FPA (MFPA). The algorithms are compared in optimizing the longitudinal vehicle model and controller using the CARLA simulator, and stability tests are also done for each algorithm. In addition, the characteristics of several cost functions in controller optimization are inspected. The results show that the MFPA is the most stable algorithm, the proposed model represents the system satisfactorily, and optimizing the controller using a regularized cost function leads to better overall performance. Our code is available in https://github.com/fadamsyah/Particle-Based-Optimization-for-Longitudinal-Control.

New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models

The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380–1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm.

Modified Flower Pollination Optimization Based Design of Interval Type-2 Fuzzy PID Controller for Rotary Inverted Pendulum System

The Type 2 Fuzzy Logic System (T2FLS) is an enhanced form of the classical Fuzzy Logic System (FLS). The T2FLS based control technics demonstrated a lot of improvements for the past few decades. This is based on the advantage of its membership function (MF). Many experimental studies indicated the superiority of Type 2 Fuzzy Logic Controller (T2FLC) over the ordinary Type 1 Fuzzy Logic Controller (T1FLC), particularly in the event of non-linearities and complex uncertainties. However, the organized design method of T2FLCs is still an interesting problem in the control engineering community. This is due to the difficulties in computing the parameters associated it. A novel application of the Modified Flower Pollination (MFP) optimization algorithm in the design of T2FL is presented. The optimized Cascade Interval Type 2 Fuzzy PID Controller (IT2FPIDC) structure is proposed in this study. The best values of the parameters of the antecedent MFs and the PID gains of IT2FPIDC are found using the MFP algorithm. The MFP optimization technique was used because of its lower computational effort and high convergence speed, in view of the higher number of variables to be optimized in cascaded IT2FPIDC. The MFP-based Type-1 Fuzzy Proportional Integral Derivative Controller (T1FPIDC) is compared with the proposed MFP-based cascade-optimized IT2FPIDC. The rotary inverted pendulum (RIP) which is a non-minimum phase, non-linear, and unstable system is employed as a benchmark for testing the proposed controller. Balance and trajectory-tracking controls of the RIP are considered. Furthermore, the disturbance rejection ability of the proposed controller is analysed. The presented control methos is evaluated on the RIP manufactured by Quanser over many simulations and real-world experiments. The performance characteristics considered are steady state error (Ess), settling time (ts), maximum overshoot (Mp) and rise time (tr). The improvement of the effectiveness and robustness proposed controller in the presence of load disturbance, noise effects and parameter variation is shown.

Modified Flower Pollination Algorithm for ICMPv6-Based DDoS Attacks Anomaly Detection

Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4*1038 unique IP addresses of devices in the network. IPv6 needed a number of protocols such as the Address Auto-configuration scheme and Internet Control Message Protocol (ICMPv6), IPv6 is vulnerable to numerous attacks such as Denial of Service (DoS) and Distributed Denial of Service (DDoS), DDoS is one of the most dangerous attacks carried out through ICMPv6 messages that impose security and pecuniary implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system of the security of a network that detects suspicious activities and deals with a massive amount of data comprised of repetitive and inappropriate features which affect the detection rate. A feature selection technique helps to reduce the computation time and complexity by selecting the optimum subset of features. This paper proposes a method for detecting ICMPv6 DDoS attacks using a modified Flower Pollination Algorithm (MFPA). The main purpose of the proposed method, first MFPA is to select the most relevant features from the ICMPv6 dataset to the detect ICMPv6 DDoS attacks. Second, an enhanced detecting DDoS flooding attacks model based on a multi-objective FPA has proposed. The results show that the first proposed method (MFPA) achieved the best accuracy equal to 97.96% with 10 features and the second proposed method Multi-Objective FPA achieved the best accuracy 97.01% with 5 features.

Robust Modified Flower Pollination Algorithm for Power Quality Enhancement in an Autonomous 31-Level Cascaded H-Bridge Photovoltaic Inverter with Partial Shading Conditions

The effect of global warming and the scarcity of fossil fuels has created an enormous problem in today’s era. To overcome such a problem, renewable energy sources, particularly solar energy, play a crucial role in meeting the developing need for power. However, the design of the Solar Photovoltaic (PV) system is interrupted by various factors such as the effect of temperature, isolation, aging, partial shading conditions, etc. Among all the factors mentioned, partial shading results in the significant diminution of power. To address this shading effect and enhance the flexibility of the PV system in terms of better utilization and energy extraction, a 31-Level Cascaded H-Bridge Multilevel Inverter (CHB-MLI) has been implemented to the autonomous PV system comprising of Maximum Power Point Tracking (MPPT) controller, boost converter and variable loads in MATLAB/Simulink architecture. To track maximum power from PV during varying irradiance and temperature and to further improve the system performance in terms of better convergence speed, an MPPT system with a Modified Flower Pollination Algorithm (MFPA) based PID controller has been proposed in this paper. To justify the suggested approach, the is-landed PV system is led to variation in irradiance and load. A detailed comparison of the proposed MFPA technique with classical control techniques has been meticulously discussed. The results obtained indicate that the suggested MFPA tuned PID with MLI outperforms the conventional methods in better system stability, reduced harmonics, and enhanced capacity to track maximum power from the PV system. In addition to this, the Total Harmonic Distortion (THD) using Fast Fourier Transform (FFT) has been found to verify IEEE-1547 power quality constraints. The values are found to be well within limits, thus justifying its real-time applications.

Modern Temperature Control of Electric Furnace in Industrial Applications Based on Modified Optimization Technique

In this paper, an enhanced version of whale optimization algorithm (EWOA) is presented to be applied in adaptive control techniques as a parameter tuner. One weakness point in this control scheme is the low efficiency of its objective function. Balloon effect (BE) is a modification introduced to increase the efficiency of the objective function of the optimization method and the ability of the controller to deal with system problems increase consequently. Controlling of the temperature of electric furnaces is considered as one of the important issues in several industrial applications. Conventional controllers such as PID controller cannot deal efficiently with the problem of parameters variations and step disturbance. This paper proposes an adaptive controller, in which the gain of the temperature controller is tuned online using EWOA supported by balloon effect. System responses obtained by the proposed adaptive control scheme using EWOA + BE have been compared with an electric furnace temperature control (EFTC) scheme response using both the PID controller-based modified flower pollination algorithm (MoFPA) and PID-accelerated PIDA-based MoFPA. From the results, it can be observed that the proposed controller tuned by the EWOA + BE method improves the time performance compared with the other techniques (PID and PIDA-based MoFPA) in case of EFTC application.

Adaptive FPA Algorithm based OPF with Unified Power Flow Controller

In this work a novel modified flower pollination algorithm has been developed to solve the problem of single and multi-objective Optimal Power Flow operations for Unified power Flow Controller in Flexible Alternating Current Transmission Systems. In the proposed Adaptive Flower Pollination Algorithm the best initial solution can be chosen from the fittest and also the weights are adaptively adjusted to get better convergence characteristics. The nature of the objective functions is non-linear and difficult to get best possible solutions within the boundary conditions of total power demand. The weak nodes are determined in the system to locate the UPFC with Fuzzy approach considering input parameters as L-Index and voltage magnitudes. The projected method is validated using IEEE-30 and IEEE-57 bus systems for three objective functions, namely, system real power loss minimization, fuel cost minimization and the combination of total generating cost and system real power loss. Results of Fuzzy- Adaptive Flower Pollination Algorithm based OPF optimization for UPFC produced optimum results for the considered objectives of total fuel cost, real power loss and for the multiobjective.

Modified flower pollination algorithm for an off-grid solar photovoltaic system

This Operating the solar photovoltaic (PV) system at its maximum power point (MPP) under numerous environmental conditions to extract the maximum power is a challenging task. The challenge is to track the MPP, especially under partial shading conditions (PSC), where the formation of multiple MPP occurs in the characteristic curve of a PV array. Nevertheless, achieving this would benefit us with optimal power production, reducing the payback time and initial cost of the PV system. To perform this duty, an electronic circuit ruled by an algorithm is employed. The MPP tracking (MPPT) algorithms can be categorized into conventional and nature inspired. The conventional algorithms can successfully track the MPP under uniform weather conditions (UWC), and unable to identify the global MPP (GMPP) under PSC. However, the nature inspired algorithms possess the ability to perform efficiently under all weather conditions. Considering this strength of nature inspired algorithms, one of the top performing algorithms named as Flower pollination algorithm (FPA) is selected based on its brilliant searching strategy in adjacent and distant locations. In this paper, some structural modifications have been proposed in the FPA to further improve its searching capability and get more quick, accurate and efficient results for the MPPT of solar PV system. Results have proven the superiority of the proposed Modified FPA (MFPA) over the FPA in terms of efficiency, accuracy, tracking speed, energy conservation, economic saving, and payback time. Simulation is performed in MATLAB/Simulink.

Optimal design of Savonius wind turbine blade based on support vector regression surrogate model and modified flower pollination algorithm

Blade shape has a significant effect on the wind-capturing ability of the Savonius wind turbine. This paper proposes an intelligent optimization method for optimizing the shape of the blade of the Savonius wind turbine. This method firstly uses the cubic Bezier curve with four design parameters (x1, y1, x2, y2) to characterize the complex blade shape, and then uses the Latin hypercube sampling method to sample some design schemes throughout the design space, and uses computational fluid dynamics simulation to evaluate the response value, i.e. the moment coefficient, of each scheme, and then uses the support vector regression surrogate model to describe the relationship between the design parameters and their response values, finally uses modified flower pollination algorithm to solve the surrogate model to obtain the optimal blade shape. After comparing and analyzing the optimized blade and the classical semicircular blade, it is found that compared with the classical semicircular blade, the optimized blade has a better wind-capturing ability. When the wind speed is 7 m/s and the tip speed ratio is 1, its average power coefficient Cp is significantly increased from 0.260027 to 0.277902 (about 6.87% higher). In addition, the aerodynamic performance of the optimized blade is also better than the classical semicircular blade at other tip speed ratios (TSRs = 0.6–1.2). It is shown that the wind turbine with the optimized blade has great potential in a practical application environment.

Variants of the flower pollination algorithm for inversion of schlumberger sounding curve

Flower pollination algorithm (FPA) is a nature-inspired algorithm that mimics flowering plant pollination behavior. Yang created the FPA in 2012, and it has since proven to be superior to other metaheuristic algorithms. Many FPA variants have recently been developed through modification and hybridization. This paper provides FPA variants consisting of Modified Flower Pollination Algorithm (MFPA), elitism Flower Pollination Algorithm (eFPA), Dimension by Dimension Improvement Flower Pollination Algorithm (DDIFPA) and Flower Pollination Algorithm with Bee Pollinator (BPFPA). Validation of the code that has been created is done through simple model testing. All algorithms provide inversion results that are consistent with true value. The results of two synthetic models indicate that eFPA is the only algorithm that can reach the global optimum. Besides having the best level of accuracy, eFPA also has the best stability.

Metaheuristic-based Near-Optimal Fractional Order PI Controller for On-grid Fuel Cell Dynamic Performance Enhancement

Fractional-order PI (FOPI) is considered a possible efficient alternative controller to classical PI controller for fuel cell (FC) on-grid systems applications. This paper aims to enhance the dynamic performance of grid-connected FC systems while employing different metaheuristic optimization techniques to FOPI controllers and consequently improve the power quality and stability of the system. The proposed metaheuristic techniques are modified flower pollination algorithm (MFPA), harmony search (HS), and electromagnetic field optimization (EFO). In the study, two FOPI-based regulators are suggested for controlling the current and the power flow between the FC and the network. For this purpose, the inverter's pulse width modulation (PWM) regulation is performed while integrating FC systems into the network. The three metaheuristic optimizers are used for the optimal gain scheduling of the FOPI controller parameters while minimizing the absolute value of the error as the cost function. To demonstrate how efficient the FOPI controllers are, fair comparisons for the on-grid FC-based system performance while proposing the different optimized advanced FOPI and conventional PI controllers are analyzed. According to the simulation results, the most efficient metaheuristic technique is identified based on an impartial defined criterion regarding the dynamic behaviors’ steady-state error, overshoot besides both setting and execution time. Through the suggested criterion, the EFO is considered the best optimization approach. Through the numerical analysis, the power quality assessment of the optimally controlled overall FC on-grid systems is illustrated through voltage sag/swell studies.

Ferroresonance Overvoltage Mitigation Using STATCOM for Grid-connected Wind Energy Conversion Systems

We present the ferroresonance overvoltage mitigation concerning the power systems of the grid-connected wind energy conversion systems (WECSs). WECS is considered based on a doubly-fed induction generator (DFIG). Ferroreso-nance overvoltage associated with a single-pole outage of the line breaker is mitigated by fast regulating the reactive power using the static compensator (STATCOM). STATCOM controller is introduced, in which two incorporated proportional-inte-gral (PI) controllers are optimally tuned using a modified flower pollination algorithm (MFPA) as an optimization technique. To show the capability of the proposed STATCOM controller in mitigating the ferroresonance overvoltage, two test cases are introduced, which are based on the interconnection status of the power transformer used with the grid-connected DFIGs. The results show that the ferroresonance disturbance can occur for the power transformers installed in the wind farms although the transformer terminals are interconnected, and neither side of the transformer is isolated. Furthermore, as a mitigation method of ferroresonance overvoltage, the proposed STATCOM controller succeeds in improving the system voltage profile and speed profile of the wind turbine as well as protecting the system components against the ferroresonance overvoltage.

Modified Flower Pollination Algorithm based Resource Management Model for Clustered IoT Network

Internet of Things (IoT) is a technological innovation that defined interaction and computation of latest period. The objects of Internet of Things would empower by embedded gadgets whose limited sources has to be managed effectively. IoT usually means a network of devices connected through wireless network and interacts through internet. Resource management, particularly energy management, becomes a serious problem while devising IoT gadgets. Numerous researchers stated that routing and clustering were energy effectual solutions for optimum resource management in IoT setting. This study introduces a Modified Flower Pollination Algorithm based Resource Management (MFPA-RMM) model for Clustered IoT Environment. The presented MFPA-RMM model majorly focuses on the clustering the IoT devices in such a way that the resources are proficiently managed. The MFPA-RMM model is derived based on the fuzzy c-means (FCM) with FPA. The FPA approach is called heuristic algorithm has benefits of global optimization and faster convergence, therefore it was incorporated to FCM system for resolving the advantages and disadvantages of FCM method termed FCM-FPA mechanism. The result analysis of the MFPA-RMM model reported the enhanced performance of the MFPA-RMM model over other well-known techniques like LEACH and TEEN.

An Improved Flower Pollination Algorithm for Global and Local Optimization

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.

Modified Flower Pollination Algorithm for Disease Identification in Swine

Modified Flower Pollination Algorithm for Disease Identification in Swine