Pollination Process Of Flowers Research Articles

Analysis of Pollination Process between Flowers and Honeybees to Derive Insights for the Design of Microrobots.

Pollination is a crucial ecological process with far-reaching impacts on natural and agricultural systems. Approximately 85% of flowering plants depend on animal pollinators for successful reproduction. Over 75% of global food crops rely on pollinators, making them indispensable for sustaining human populations. Wind, water, insects, birds, bats, mammals, amphibians, and mollusks accomplish the pollination process. The design features of flowers and pollinators in angiosperms make the pollination process functionally effective and efficient. In this paper, we analyze the design aspects of the honeybee-enabled flower pollination process using the axiomatic design methodology. We tabulate functional requirements (FRs) of flower and honeybee components and map them onto nature-chosen design parameters (DPs). We apply the "independence axiom" of the axiomatic design methodology to identify couplings and to evaluate if the features of a flower and a honeybee form a good design (i.e., uncoupled design) or an underperforming design (i.e., coupled design). We also apply the axiomatic design methodology's "information axiom" to assess the pollination process's robustness and reliability. Through this exploration, we observed that the pollination process is not only a good design but also a robust design. This approach to assessing whether nature's processes are good or bad designs can be valuable for biomimicry studies. This approach can also inform design considerations for bio-inspired innovations such as microrobots.

Optimal STATCOM Design via Flower Pollination Approach for A Multimachine Power System

A new metaheuristic method, the Flower Pollination Approach (FPA), based on the pollination process of flowers is proposed in this article for the optimal design of a static synchronous compensator (STATCOM) in a multimachine environment. The STATCOM parameter tuning process is converted to an optimization problem which is solved by FPA. The performance of the proposed FPA-based STATCOM (FPASTATCOM) is compared with Genetic Algorithm (GA) based STATCOM (GASTATCOM) under various operating conditions and disturbances. The superiority of the proposed technique in damping oscillations is confirmed via eigenvalues and time domain simulation results over the GA.

Particle swarm methods for transport and logistics optimization processes

This article describes heuristic approaches to static optimization methods, taking into account the nature of the motion of a swarm of particles representing, for example, birds, ants, bees, fireflies, bats, krill, cuckoos, cuttlefish, cockroaches, or the pollination process of flowers. In the given descriptions of the methods, the features of swarm intelligence are detailed, the optimization quality indicators are formulated, and flow charts of the computational algorithms are provided.

Flower pollination based dental image segmentation

ABSTRACT Flower pollination-based algorithm (FPA), inspired from the pollination process of flowers, is a population-based metaheuristic optimisation algorithm for solving real-world problems. This article introduces the feature of insectivorous plants in catching the pollinators (preys) with the solution procedure of FPA for avoiding sub-optimal traps and models the dental image segmentation problem as an optimisation problem. This enhanced FPA (EFPA) is then applied in solving the formulated multilevel segmentation problem of dental images. The proposed EFPA-based method optimises the threshold values for dental images by effectively exploring the problem space. The segmented images along with PSNR values of the proposed method are compared with FPA-based and OTSU-basedsegmentation methods for exhibiting its superior performance.

Pollination of Turnera subulata: exotic or native bees?

ABSTRACT This work analyzed floral visitors of Turnera subulata Sm. (Turneraceae) within an anthropized area in Bahia (UEFS Campus), focusing on potential pollinators, fruit and seed production and the influence of climatic factors on this interaction. The study was carried during six months in 2018 and 2019. Aspects of floral biology and visitor behavior were observed. Turnera subulata flowers lasted approximately six hours and during this period they were exposed to visitors at different intervals to assess the peak time of fruit formation. The most frequent visitors were the bees Apis mellifera Linnaeus, 1758, Trigona sp., Augochlora sp. and Protomeliturga turnerae (Ducke, 1907). The foraging peak occurred in the morning, coinciding with the time of greatest formation of fruits and seeds, and with the time of greatest number of open flowers. Both exotic and native bees acted as potential pollinators, being considered complementary in the flower pollination process.

Flower Pollination Algorithm for Vehicle Routing Problem with Time Windows (VRPTW)

Vehicle Routing Problem with Time Windows (VRPTW) is a vehicle route deciding problem that is used in order to serve customer who involved more than one vehicle with a limited time, so as a minimum distance route is obtained without disobeying vehicle capacity cargo restriction and time range. Flower Pollination Algorithm (FPA) is an algorithm which inspires from nature and that is flower pollination process toward a plant. Within an FPA, there are two main steps to use, they are global flower pollination and local flower pollination. Those two steps are determined by using switch probability parameter. This program is made in Java language program to apply FPA in solving VRPTW which is implemented in three example cases, they are small-scale datum with 25 customers, medium-scale datum with 50 customers, and big-scale datum with 100 customers. According to the results, it can be concluded that the larger number of flowers and iterations can affect the number of total minimum travel distance become smaller. Furthermore, a better total minimum travel distances also will be obtained if the value of switch probability parameter is larger.

A Hybrid Hyper-Heuristic Flower Pollination Algorithm for Service Composition Problem in IoT

Objectives: The modern science applications have non-continuous and multivariate nature due to which the traditional optimization methods suffer a lack of efficiency. Flower pollination is a natural interesting procedure in the real world. The novel optimization algorithms can be designed by employing the evolutionary capability of the flower pollination to optimize resources. Methods: This paper introduces the hybrid algorithm named Hybrid Hyper-Heuristic Flower Pollination Algorithm, HHFPA. It uses a combination of Flower Pollination Algorithm (FPA) and Hyper- Heuristic Evolutionary Algorithm (HypEA). This paper compares the basic FPA with the proposed algorithm named HHFPA. FPA is inspired by the pollination process of flowers whereas the hyper-heuristic evolutionary algorithm operates on the heuristics search space that contains all the heuristics to find a solution for a given problem. The proposed algorithm is implemented to solve the Quality of Service (QoS) based Service Composition Problem (SCoP) in Internet of Things (IoT). With increasing services with same functionality on the web, selecting a suitable candidate service based on non-functional characteristics such as QoS has become an inspiration for optimization. Results: This paper includes experimental results showing better outcomes to find the best solution using the proposed algorithm as compared to Basic FPA. Conclusion: The empirical analysis also reveals that HHFPA outperformed basic FPA in solving the SCoP with more convergence rates.

Modified Flower Pollination-based Segmentation of Medical Images

This paper presents a modified flower pollination-based method for performing multilevel segmentation of medical images. The flower pollination-based optimization (FPO) models the pollination process of flowers. Bees serve a major role in the pollination activity of flowers and they memorize and recognize the best flowers producing large pollens of nectar. Such memorizing ability of bees is adapted in the FPO for improving the exploration ability of the algorithm. Besides, the mechanism of avoiding predators by pollinators is also included in the modified FPO (MFPO) for getting away from sub-optimal traps. The medical image segmentation problem is transformed into an optimization problem and solved using the modified FPO (MFPO). The method explores for optimal thresholds in the problem space of the given medical image. The segmented images are presented for showing the superior performance of the proposed method.

Hybrid Flower Pollination Algorithm with Sequential Quadratic Programming Technique for Solving Dynamic Combined Economic Emission Dispatch Problem

This paper presents the solution of highly complex, non-linear, multi-objective Dynamic Combined Economic Emission Dispatch (DCEED) problem. DCEED is a power system optimization problem with conflicting objectives of fuel cost and emission. DCEED includes constraints like valve point loading effect, Transmission Losses and Ramp Rate limits. Solution of DCEED problem is given by a novel Hybridized Flower Pollination Algorithm (FPA) with Sequential Quadratic Programming (SQP). FPA is a nature inspired population based meta-heuristic optimization technique that models its search on the flower pollination process. The non-convex nature of generation because of numerous operational, physical and dynamic constraints, makes search space highly multi model and complex. This makes DCEED a challenging as well as an attractive problem for research. The effectiveness of FPA-SQP is tested and validated by applying it on IEEE Standard 5-unit and 10-unit non-convex test system in MATLAB environment for the time interval of 24 hours. The results achieved by this algorithm show significant reduction in cost and emission as compared to other available techniques in the literature.

Optimal capacitor placement in radial distribution systems using flower pollination algorithm

In this paper, a new metaheuristic algorithm that mimics the pollination process of flowers and is known as the flower pollination algorithm (FPA) has been proposed for the solution of the optimal capacitor placement (OCP) problem in a radial distribution system (RDS). The objective of this problem is to minimize the total power loss and cost of capacitor installation by optimal location and sizing of the capacitors. OCP also improves the voltage profile. In this work, the power flow and losses in the network are obtained with the help of load flow analysis using data structures. The proposed FPA approach has been applied to solve 33-, 34-, 69- and 85 bus RDSs. The results obtained are compared with those of the analytical method, fuzzy real coded genetic algorithm, two stage fuzzy approach and teaching learning based optimization algorithms. The comparison shows that FPA is a highly suitable optimization approach for solving capacitor placement problems.

An Optimal Design of CMOS Two Stage Comparator Circuit using Swarm Intelligence Technique

A swarm intelligent based optimization technique named as Flower pollination algorithm (FPA) is applied for the design of the CMOS two stage comparator circuit. The basic idea of FPA mimics the flower pollination process of flowering plants. The input control parameters of FPA improve the exploration and exploitation capabilities of optimization problem. This paper presents the design of a CMOS two-stage comparator circuit using simulation based model called swarm intelligence technique. Simulation results shows that the proposed method is capable to determine the transistor sizes and bias current values of the CMOS comparator. The results obtained from the FPA improved the design performance of comparator in terms of power consumption, MOS transistor area and gain. To investigate the efficiency of proposed approach, comparisons have been carried out with differential evolution (DE) and harmony search (HS) algorithm based circuit design. The performances of FPA based comparator design are better than the previously reported works

An Optimal Design of CMOS Two Stage Comparator Circuit using Swarm Intelligence Technique

A swarm intelligent based optimization technique named as Flower pollination algorithm (FPA) is applied for the design of the CMOS two stage comparator circuit. The basic idea of FPA mimics the flower pollination process of flowering plants. The input control parameters of FPA improve the exploration and exploitation capabilities of optimization problem. This paper presents the design of a CMOS two-stage comparator circuit using simulation based model called swarm intelligence technique. Simulation results shows that the proposed method is capable to determine the transistor sizes and bias current values of the CMOS comparator. The results obtained from the FPA improved the design performance of comparator in terms of power consumption, MOS transistor area and gain. To investigate the efficiency of proposed approach, comparisons have been carried out with differential evolution (DE) and harmony search (HS) algorithm based circuit design. The performances of FPA based comparator design are better than the previously reported works

RETRACTED: Multi-spectral and panchromatic image fusion approach using stationary wavelet transform and swarm flower pollination optimization for remote sensing applications

Abstract This paper proposes a multi-spectral (MS) and panchromatic (Pan) image fusion approach based on the flower pollination algorithm optimization (FPA). The FPA is used to get an optimal fused image. The image fusion quality depends on the choice of the weight of fusion rule. The proposed approach uses FPA to optimize the weights of a fusion rule to make a perfect image fusion process. FPA is a nature-inspired algorithm, based on the characteristics of a flower pollination process. FPA averts trapping in local optimal solution. In this paper, the remote sensing image fusion based on flower pollination algorithm is compared to several states of the art image fusion approaches including Intensity-hue-saturation (IHS) image fusion; stationary wavelets transform image fusion based on the average weight fusion rule (SWT-AW) and the image fusion based on the particle swarm optimization (PSO). The experimental results used MODIS satellite series with spatial resolutions 250 m, 500 m, and 1 km, which are low spatial resolution and multispectral images; and Pan image of SPOT satellite is high spatial resolution 10 m to produce synthetic imagery at SPOT spatial resolutions and MODIS multispectral resolution at the same time. The experimental results prove that the proposed remote sensing image fusion approach can illustrate a better performance than the other approaches. The experimental results show that the approach offers up to 20% enhancement in Peak Signal to Noise Ratio (PSNR), 1% enhancement in Structural Similarity Index (SSIM), 1% and 0.5% enhancement in entropy information (EI) than best existing particle swarm optimization (PSO) approach. The results indicate that the proposed approach outperforms over existing approaches.

Flower Pollination Algorithm Based PDFF Controller for a Two-Area Interconnected Thermal Power System with Gas / Diesel Units

This paper presents a new approach for designing a Pseudo Derivative Feed Forward (FDFF) controller for the load- frequency control of the interconnected power system comprising Thermal power system and Gas / Diesel power plants. The proposed PDFF controller is designed to improve the dynamic performance of the frequency and tie line power under a sudden load disturbance in an area with the computation of Ancillary Service Requirement Assessment Indices (ASRAI). The PDFF controller is optimized using Flower Pollination Algorithm (FPA) which is based on the quality of pollination process of flowers. The optimized PDFF controller is implemented to bring back the frequency to stable state and the net interchanges to their desired values for each control area in the shortest possible time based on the settling time and peak over shoot concept of control input deviations of each area. Simulation result reveals that the interconnected thermal power system with Gas power plant ensures a better dynamic and steady state performance than that of the system incorporated with Diesel power plant.

Flower Pollination Algorithm for Automatic Generation Control in Interconnected Power Systems under Restructured Market – A Survey

Flower pollination is a fascinating progression in the world based on pollination process of plants. Its evolutionary features can be used to widen new optimization techniques and algorithms. In this paper, a new algorithm called Flower Pollination Algorithm (FPA) is to be considered for optimizing the power system problems which is stimulated by the pollination process of flowers. Test functions are used to validate the new algorithm adopted in two area deregulated power system. This algorithm is used to tune and to determine the gains of the controllers in deregulated systems.

Reconfiguration and Capacitor Placement of Radial Distribution Systems by Modified Flower Pollination Algorithm

This article proposes a combined methodology for network reconfiguration and optimal placement of shunt capacitors in radial distribution systems to reduce real power loss and enhance bus voltages. The power loss is reduced by network reconfiguration and capacitor placement, which in turn reduces a utility's loss of revenue. To ensure radial structure and avoid islanding of nodes, feasible tie-switch combinations are formed prior to the optimization process using a graph theory based method. The modified flower pollination algorithm uses a flower pollination process, an improved local neighborhood search method, and a dynamic switching probability approach to enhance the global search for optimization. The performance of this approach is tested on standard 33-bus, 69-bus, and 118-bus radial distribution test feeders. Results have been compared with previous methods reported in the literature, indicating the effectiveness of the combinatorial approach in terms of loss reduction, voltage enhancement, and cost saving.

Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search

This paper presents a multi-objective retinal blood vessels localization approach based on flower pollination search algorithm (FPSA) and pattern search (PS) algorithm. FPSA is a new evolutionary algorithm based on the flower pollination process of flowering plants. The proposed multi-objective fitness function uses the flower pollination search algorithm (FPSA) that searches for the optimal clustering of the given retinal image into compact clusters under some constraints. Pattern search (PS) as local search method is then applied to further enhance the segmentation results using another objective function based on shape features. The proposed approach for retinal blood vessels localization is applied on public database namely DRIVE data set. Results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of accuracy, sensitivity, and specificity with many extendable features.

Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm

In this paper, a flexible power system planning strategy using a novel population-based metaheuristic algorithm inspired by the pollination process of flowers named adaptive flower pollination algorithm (APFPA) has been proposed. The proposed power system planning strategy implemented and successfully applied for solving the security optimal power flow (OPF) considering faults at critical generating unit. The main particularity of the proposed variant is that the control variables are optimized based on an adaptive and flexible structure. Also the performances of the standard FPA is improved by dynamically adjusting their control parameters, this allows creating diversity and balance between exploration and exploitation during search process. The robustness of the proposed planning strategy, is demonstrated on the IEEE 30-Bus, and IEEE 57-Bus tests power system for different objectives such as fuel cost, power losses, and voltage deviation. Considering the quality of the obtained results compared with various recent methods reported in the literature, the proposed strategy seems to be a competitive tool for solving with accuracy the security OPF considering critical situations.

An Innovative Approach for Attribute Reduction Using Rough Sets and Flower Pollination Optimisation

Optimal search is a major challenge for wrapper-based attribute reduction. Rough sets have been used with much success, but current hill-climbing rough set approaches to attribute reduction are insufficient for finding optimal solutions. In this paper, we propose an innovative use of an intelligent optimisation method, namely the flower search algorithm (FSA), with rough sets for attribute reduction. FSA is a relatively recent computational intelligence algorithm, which is inspired by the pollination process of flowers. For many applications, the attribute space, besides being very large, is also rough with many different local minima which makes it difficult to converge towards an optimal solution. FSA can adaptively search the attribute space for optimal attribute combinations that maximise a given fitness function, with the fitness function used in our work being rough set-based classification. Experimental results on various benchmark datasets from the UCI repository confirm our technique to perform well in comparison with competing methods.

An Application of Flower Pollination Algorithm to Solve Combined Economic Emission Dispatch by Considering Valve-Point Loading Effect

Flower Pollination Algorithm (FPA) is a new biologically inspired meta-heuristic optimization technique based the pollination process of flowers. FPA mimics the flower pollination characteristics in order to survival by the fittest. This paper presents implementation of FPA optimization in solving Combined Economic Emission Dispatch (CEED) problems in power system. CEED actually is a bi-objective problem where the objective of economic dispatch (ED) and emission dispatch (EMD) are combined into a single function by using price penalty factor. Hence, CEED is used to minimize the total generation cost by minimizing fuel cost and emission concurrently and at the same time determines the optimum power generation. In this paper, the valve point loading effect problem in power system also will be considered. The proposed algorithm are tested on four different test systems which are: 6-generating unit and 11-generating unit without valve point effect with no transmission loss, 10-generating unit with having valve point effect and transmission loss, and lastly 40-generating unit with having valve point effect without transmission loss. The results of these four different test cases were compared with the optimization techniques reported in recent literature in order to observe the effectiveness of FPA. Result shows FPA able to perform better than other algorithms by having minimum fuel cost and emission