Multi-objective Flower Pollination Algorithm Research Articles

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

Single- and Multi-Objective Optimization Frameworks of Shape Design of Tubular Linear Synchronous Motor

The shape design of the Tubular Linear Synchronous Motor (TLSM) is a critical engineeri ng optimization problem which was handled as single- and multi-objective optimization frameworks. However, the different practical constraints for the TLSM design must be efficiently guaranteed. This paper proposes a developed multi-objective shape design of the TLSM to maximize the operating force and minimize the flux saturation. In this regard, a Multi-objective Grey Wolf Optimizer (MGWO) is developed, including an outside archive with a predetermined size that is integrated for storing and retrieving Pareto optimal solutions. Using this knowledge, the grey wolf social structure would then be established, and, in the multi-objective searching environments, grey wolf hunting behavior would then be replicated. The superiority and effectiveness of the developed MGWO is assessed in comparison to the Multi-objective Flower Pollination Algorithm (MFPA), Multi-objective Lichtenberg Algorithm (MOLA), and Multi-objective Grasshopper Optimization Algorithm (MGOA). The outcomes illustrate that the developed MGWO provides an average improvement of 73.46%, 19.07%, and 15.15% compared to MFPA, MOLA, and MGOA, respectively. The validation of the developed MGWO is extended for a multi-objective form of welded beam design (WBD) by simultaneously minimizing the deflection and the manufacturing costs. Similar findings are obtained with different reference points, the developed MGWO provides an average improvement of 2.8%, 0.7%, and 3.04% compared to MFPA, MOLA, and MGOA, respectively.

A new multi-objective optimization algorithm based on combined swarm intelligence and Monte Carlo simulation

Currently, multi-objective optimization is an important problem in various fields. This paper proposes an innovative multi-objective flower pollination algorithm combined with Monte Carlo simulation (called MFPAMC). MFPAMC incorporates the following three technologies: the flower pollination algorithm (FPA), the Monte Carlo method (MC), and a sorting function. Initially, the FPA is used to search for the optimal solution, which improves the search efficiency. When the solution obtained by the FPA is no longer updated, MC simulation is adopted to further find the optimal solution. The sorting function prevents the loss of objective function information, and the result obtained is objective and practical. A test function is used to verify whether the combination of the FPA and MC simulation can improve the search accuracy. The results confirm that the search accuracy is improved. Finally, MFPAMC is used for empirical analysis, and the experimental results are compared with those of four other methods. The comparison results confirm that the optimized results are ideal and further indicate that MFPAMC has significant potential for practical application.

Multi-objective flower pollination algorithm: a new technique for EEG signal denoising

Multi-objective flower pollination algorithm: a new technique for EEG signal denoising

Multi-objective flower pollination algorithm applied to 5G vehicular networks communication

The Cellular Vehicle-to-Everything (C-V2X) technology, as a widest version of Vehicular Ad-hoc Network (VANET), aims to interconnect vehicles and any other latest technological infrastructures. In this context, the fifth generation of mobile networks (5G) based on millimeter waves (mmWave) is an excellent alternative for the implementation of vehicular networks, mainly because it is capable of providing high data rates (Gbps) and ultra-low latency, requirements of C-V2X. On the other hand, mmWave signals are highly susceptible to blocking, causing low quality of service (QoS) in VANETs, compromising network functionality and the safety of drivers and pedestrians. Thus, in this work evolutionary computing techniques are applied in the simulation of a 5G vehicular network based on millimeter waves, exploring Media Access Control (MAC) sublayer parameters to optimize packet loss, latency and throughput, in order to optimize inter-vehicular communication. The Multi-objective Flower Pollination Algorithm (MOFPA) was used for this purpose. The results obtained show that the adopted approach can reach results close to the optimal pareto of non-dominated solutions, with a 75% reduction in exploration time in relation to the exhaustive search process. Finally, the performance of the metaheuristics adopted is compared with the non-dominated genetic classification algorithm (NSGA-II) and the multi-objective differential evolutionary algorithm (MODE).

Multi-objective metaheuristic search algorithms for service composition in IoT

The basic idea behind the Internet of Things (IoT) is to connect every physical Thing to the Internet. IoT adds ability in Things to sense and communicate with other Things. One of the main concerns in IoT is forming an optimal service composition to fulfil the user requirements while balancing the quality of service (QoS) parameters. So, in this paper, the service composition problem in IoT has been addressed using multi-objective optimisation. An optimal solution to this problem has been provided through proposed novel hybrid and proposed algorithm Multi-Objective Hybrid Hyper-Heuristic Flower Pollination Algorithm (MOHHFPA). The superiority of this algorithm is proved by empirically and statistically comparing it with existing multi-objective algorithms, namely the Non-dominated Sorting Genetic Algorithm II (NSGA II), Multi-Objective Flower Pollination Algorithm (MOFPA), and the Multi-Objective Hyper-Heuristic Search Algorithm (MOHypEA). The proposed algorithm is empirically evaluated using a real-world case study.

An effective energy flow management in grid-connected solar–wind-microgrid system incorporating economic and environmental generation scheduling using a meta-dynamic approach-based multiobjective flower pollination algorithm

In this research paper, we focus on developing a generation scheduling model using an intelligent soft-computing technique in a microgrid (MG) system. A multiobjective power management system with innovative features of the MG technology is presented The necessity and reason for undertaking this study is to optimize MG operation as well as address the uncertainty of random energy production from renewables by utilizing demand response (DR) programs. A meta-dynamic-approach-based multiobjective flower pollination algorithm is applied to solve this complex, nonlinear, multiobjective optimization (MOO) problem. Energy management in MGs utilizing renewable energy is a salient feature. DR schemes are conducted in residential, commercial, and industrial customers. Simulations are performed to achieve reduced prices and minimum emissions. Comparative studies were conducted wherein the metaheuristic algorithm demonstrated superior performance and higher efficiency compared to other technique. Operating costs reduced by 20.3% and emissions reduced by 5% after the implementation of DR programs using a meta-dynamic-approach-based flower pollination algorithm compared to particle swarm optimization (PSO). The results demonstrate the superiority of the proposed demand-side management modeling method.

Multi-Objective Environmental Economic Dispatch of an Electricity System Considering Integrated Natural Gas Units and Variable Renewable Energy Sources

This paper presents a multi-objective economic-environmental dispatch (MOEED) model for integrated thermal, natural gas, and renewable energy systems considering both pollutant emission levels and total fuel or generation cost aspects. Two cases are carried out with the IEEE 30-bus system by replacing thermal generation units into natural gas units to minimize the amount of toxin emission and fuel cost. Equality, inequality like active, reactive powers, prohibited operating zones (POZs) which represents poor operation in the generation cost function, and security constraints are considered as system constraints. Natural gas units (NGUs) are modeled in detail. Therefore, the flow velocity of gas and pressure pipelines are also considered as system constraints. Multi-objective optimization algorithms, namely multi-objective Harris hawks optimization (MOHHO) and multi-objective flower pollination algorithm (MOFPA) are employed to find Pareto optimal solutions of fuel or generation cost and emission together. Furthermore, the technique for order preference by similarity to ideal solution (TOPSIS) is proposed to obtain the best value of Pareto optimal solutions. Three scenarios are investigated to validate the effectiveness of the proposed model applied to the IEEE 30-bus system with the integration of variable renewable energy sources (VRESs) and natural gas units. The results obtained from Scenario III with NGUs installed instead of two thermal units reveal that the economic dispatching approach presented in this work can greatly minimize emission levels as 0.421 t/h and achieve lower fuel cost as 796.35 $/h. Finally, the results obtained show that the MOHHO outperforms the MOFPA in solving the MOEED problem.

Compact ultra‐wideband FSS optimised through fast and accurate hybrid bio‐inspired multiobjective technique

In this study, a bioinspired computation (BIC) optimisation technique to synthesise the geometric dimensions of a lozenge-shaped broadband frequency selective surface (FSS) for electromagnetic wave filtering in satellite communications systems, for the X (8–12 GHz) and Ku (12–18 GHz) bands, is presented. A hybrid BIC technique, that combines the general regression neural network with a multi-objective flower pollination algorithm, is proposed. The objectives of the optimisation process consist of tuning the resonant frequency of the lozenge-shaped FSS to 15.2 GHz and operating bandwidth to 8 GHz, characterising a broadband framework. The cutoff frequencies to control the operating frequency range of the FSS, referring to transmission coefficient in decibels (dB), were obtained at a threshold of −10 dB. The proposed lozenge-shaped FSS, after optimisation of the geometrical dimensions of its unit cell, was fabricated on fiberglass substrate (FR4). The simulated and measured results show a good agreement.

Multi-Objective Flower Pollination Algorithm Based Controller for UPQC Including Hybrid Power Source

This paper presents a new control method for Unified Power Quality Conditioner (UPQC) for effective management of the power-sharing and improvement of the quality of power. The voltage disturbances produced in the source side due to non-linear load conditions can be protected using the UPQC model. Flexible Alternating Current Transmission System (FACTS) devices were fed through a hybrid power generator that had the primary source, a Proton Exchange Membrane Fuel Cell (PEMFC) and a secondary source, a supercapacitor. In this paper, a multi-objective function (power factor, voltage sag, and the total harmonic distortion (THD)) with different control strategies have been considered. An optimization algorithm named Flower Pollination Algorithm (FPA) has used for optimizing Proportional Integral (PI) coefficients. A suitable fitness function has been developed for the FPA method and the simulation performed. The performance of the FPA method has been compared with three different algorithms, namely, particle swarm optimization algorithm (PSOA), Differential Evolution Algorithm (DEA), and Ant Colony Optimization Algorithm (ACOA). The result obtained shows the proposed FPA providing the best result compared to other methods.

Stability Improvement of VSC HVDC based Multi Machine Power System using Optimized Super Twisting Sliding Mode Controller

This article presents application of an optimized robust and nonlinear controller approach for dynamic stability of a multi machine power system integrated with VSC-HVDC transmission. To improve dynamic stability of the system, a super twisting Sliding Mode Control approach, whose gains are optimized by multi objective flower pollination algorithm is designed to enhance the system stability over a various operating conditions, such as three phase fault, dc link fault, converter and inverter parameter change, increase of the mechanical input of the generator and change of active and reactive power. The super twisting sliding mode controller is designed for its superiority in robustness and chattering free actions over conventional siding mode controller in which a hyperbolic tangent function is chosen for the sliding surface. A multi objective flower pollination algorithm is applied to find optimized gains of the super twisting Sliding mode controller, in order to improve the capacity of the controller and the dynamic stability of the system. The results are compared with STSMC and conventional PI controller. It is shown from the result that the proposed controller is more capable in settling the system in steady state from any abnormal condition quickly than SMC and PI controller.

A structure with density-weighted active learning-based model selection strategy and meteorological analysis for wind speed vector deterministic and probabilistic forecasting

Accurate wind speed forecasting ensures the controllability for the wind power system. In this paper, a structure with density-weighted active learning (DWAL)-based model selection strategy from the perspective of meteorological factors is proposed to improve the accuracy and stability for wind speed deterministic and probabilistic forecasting. To improve training efficiency and accelerate the sample selection process, DWAL is employed. The multi-objective flower pollination algorithm is used to combine best models selected from model space with optimal weights for higher accuracy and stability. Except deterministic forecasts, as large-scale wind power generation integrated into power grid, the wind direction should also be forecasted and the estimation of the wind speed and direction uncertainty is vital, offering various aspects of forecasts for risk management. Thus, both deterministic and probabilistic forecasting for the wind speed vector are included in this paper. Eight datasets from Ontario Province, Canada, are utilized to evaluate forecasting performance of the model selection and the proposed structure. Results demonstrated: (a) the proposed structure is suitable for wind speed vector forecasting; (b) the proposed structure obtains more precise and stable forecasting performance; (c) the proposed structure improves the accuracy of deterministic forecasting and provides probabilistic information for wind speed vector forecasting.

Wireless Sensor Network Deployment Optimization Based on Two Flower Pollination Algorithms

For the wireless sensor networks (WSNs) heterogeneous node deployment optimization problem with obstacles in the monitoring area, two new flower pollination algorithms (FPA) are proposed to deploy the network. Firstly, an improved flower pollination algorithm (IFPA) is proposed based on FPA, aiming at the shortcomings of the convergence speed is slow and the precision is not high enough of FPA. The nonlinear convergence factor is designed to correct the scaling factor of FPA, the Tent chaotic map effectively maintains the diversity of the population in the late iteration, and a greedy crossover strategy is designed to assist the remaining individual search with better individuals. Secondly, based on FPA, a non-dominated sorting multi-objective flower pollination algorithm (NSMOFPA) is proposed. The external archive strategy and leader strategy are introduced, to solve the global pollination problem. The proposed crowding degree method and the introduced elite strategy effectively maintain the diversity of the population. Then, IFPA is applied to WSN deployment aiming at optimizing coverage rate, simulation experiments show that IFPA can obtain a higher coverage rate with shorter iterations, which can save network deployment costs. Finally, applying NSMOFPA to the WSN deployment with optimization objectives for coverage rate, node radiation overflow rate and energy consumption rate. The experimental results verify that NSMOFPA has a good optimization effect and can provide a better solution for WSN deployment.

Classification of EEG mental tasks using Multi-Objective Flower Pollination Algorithm for Person Identification

In the modern life, the authentication technique for any system is considered as one of the most important challenges task which must careful consideration. Therefore, many researchers have developed traditional authentication systems to deal with our digital world. Recently, The Biometric techniques have been successfully provided a high level of authentication, such as fingerprint, face recognition, and voice recognition. In this paper, a new authentication system has been proposed which is based on EEG signals with hybridizing wavelet transform and multi-objective flower pollination algorithm (MOFPA-WT). The main task of MOFPA is to find the optimal WT parameters for EEG signal denoising which can extract unique features form the EEG. The proposed method (MOFPA-WT) tested using a standard EEG database which has five different mental tasks, includes baseline, multiplication, rotation, letter composing, and visual counting. To classify the EEG signals using proposed method four classification methods are applied which are, neural network, decision tree, Naive Bayes, and support vector machine. The performance of the (MOFPA-WT) is evaluated using four criteria: (i) accuracy, (ii) sensitivity, (iii) specificity, (v) false acceptance rate. The experimental results show the (MOFPA-WT) can achieve the highest recognition rates up to 85% using neural network classifier based on visual counting task as well as the EEG_ Std feature obtained the highest accuracy compared with others EEG features based on visual counting task.

Novel optimization technique of isolated microgrid with hydrogen energy storage.

This paper presents a novel optimization technique for energy management studies of an isolated microgrid. The system is supplied by various Distributed Energy Resources (DERs), Diesel Generator (DG), a Wind Turbine Generator (WTG), Photovoltaic (PV) arrays and supported by fuel cell/electrolyzer Hydrogen storage system for short term storage. Multi-objective optimization is used through non-dominated sorting genetic algorithm to suit the load requirements under the given constraints. A novel multi-objective flower pollination algorithm is utilized to check the results. The Pros and cons of the two optimization techniques are compared and evaluated. An isolated microgrid is modelled using MATLAB software package, dispatch of active/reactive power, optimal load flow analysis with slack bus selection are carried out to be able to minimize fuel cost and line losses under realistic constraints. The performance of the system is studied and analyzed during both summer and winter conditions and three case studies are presented for each condition. The modified IEEE 15 bus system is used to validate the proposed algorithm.

EEG Feature Extraction for Person Identification Using Wavelet Decomposition and Multi-Objective Flower Pollination Algorithm

In the modern life, the authentication technique for any system is considered as one of the most important and challenging tasks. Therefore, many researchers have developed traditional authentication systems to deal with our digital society. Recently, several studies showed that the brain electrical activity or electroencephalogram (EEG) signals could provide robust and unique features that can be considered as a new biometric authentication technique, given that accurate methods to decompose the signals must also be considered. This paper proposes a novel method for extracting EEG features using multi-objective flower pollination algorithm and the wavelet transform. The proposed method was applied in two scenarios for EEG signal decomposition to extract unique features from the original signals. Moreover, the proposed method is compared with the state-of-the-art techniques using different criteria with promising results.

Research and application of a combined model based on multi-objective optimization for electrical load forecasting

Short-term load forecasting (STLF) plays an irreplaceable role in the efficient management of electrical systems but remains an extremely challenging task. To achieve the goal of load forecasting with both accuracy and stability, a combined model based on a multi-objective optimization algorithm, the multi-objective flower pollination algorithm (MOFPA), is developed in this study. In this combined model, MOPFA is used to optimize the weights of single models to simultaneously obtain high accuracy and great stability, which are two mostly independent objectives and are equally important to the model effectiveness. Data preprocessing techniques, such as the fast ensemble empirical mode decomposition and multiple seasonal patterns, are also incorporated in this model. Case studies of half-hourly electrical load data from the State of Victoria, the State of Queensland, and New South Wales, Australia, are considered as illustrative examples to evaluate the effectiveness and efficiency of the developed combined model. The experimental results clearly show that both the accuracy and stability of the combined model are superior to those of the single models.

The effect of employing nanofluid on reducing the bore length of a vertical ground-source heat pump

In our era ground-source heat pumps are known as energy-efficient air conditioning systems. However, their high initial costs are a major obstacle to the widespread use of such systems. In this study, the effects of using Al2O3/water nanofluid as heat transfer fluid on reducing the bore length of a ground heat exchanger in a vertical ground-source heat pump are examined. For this purpose, the effective thermal conductivity and the effective viscosity of the nanofluid, which play prominent roles in the convective heat transfer, are optimized via using multi-objective Flower Pollination Algorithm. In this algorithm, the logic of the second version of non-dominated sorting is utilized to deal with the two objectives of this optimization. Then some of the best possible combinations of the thermal conductivity and the viscosity of the nanofluid, extracted from the obtained Pareto front, were used to compute the required bore length. The results were compared with the required bore length calculated using pure water as the heat transfer fluid in the ground heat exchanger. The comparison demonstrated that employing Al2O3/water nanofluid instead of water as heat transfer liquid reduced less than 1.3% of the bore length. Furthermore, investigating the reason of this low reduction in the bore length revealed that grout has the most potential to reduce the bore length among the heat transfer fluid, tubes, and grout.

Multi objective Flower Pollination Algorithm for solving capacitor placement in radial distribution system using data structure load flow analysis

Abstract The radial distribution system is a rugged system, it is also the most commonly used system, which suffers by loss and low voltage at the end bus. This loss can be reduced by the use of a capacitor in the system, which injects reactive current and also improves the voltage magnitude in the buses. The real power loss in the distribution line is the I2R loss which depends on the current and resistance. The connection of the capacitor in the bus reduces the reactive current and losses. The loss reduction is equal to the increase in generation, necessary for the electric power provided by firms. For consumers, the quality of power supply depends on the voltage magnitude level, which is also considered and hence the objective of the problem becomes the multi objective of loss minimization and the minimization of voltage deviation. In this paper, the optimal location and size of the capacitor is found using a new computational intelligent algorithm called Flower Pollination Algorithm (FPA). To calculate the power flow and losses in the system, novel data structure load flow is introduced. In this, each bus is considered as a node with bus associated data. Links between the nodes are distribution lines and their own resistance and reactance. To validate the developed FPA solutions standard test cases, IEEE 33 and IEEE 69 radial distribution systems are considered.

Bi-criteria parallel machine scheduling using nature-inspired hybrid flower pollination algorithm

Parallel machine scheduling and its complex combinatorial optimisation problems are widely studied. A lot of research has been carried out in this area to determine how to effectively schedule parallel execution of different jobs to be carried out using different machines in order to achieve the optimisation criteria. In this paper, the formulations of multi-objective flower pollination algorithm obtained by the combination of weighted objectives or secondary storage for managing possible solutions along with their hybrids with genetic algorithm have been developed and applied to the problem of scheduling jobs on machines running in parallel in order to optimise bi-criteria, namely, maximum tardiness and weighted flow time. It has been empirically verified that multi-objective flower pollination algorithm using secondary storage and hybridised with genetic algorithm leads to better results as compared to the existing algorithms. The results obtained have been verified by numerical illustrations and statistical tests.