Modified Fruit Fly Optimization Research Articles

Hyperparameter Tuned Deep Autoencoder Model for Road Classification Model in Intelligent Transportation Systems

Unmanned aerial vehicles (UAVs) have significant abilities for automatic detection and mapping of urban surface materials due to their high resolution. It requires a massive quantity of data to understand the ground material properties. In recent days, computer vision based approaches for intelligent transportation systems (ITS) have gained considerable interest among research communities and business people. Road classification using remote sensing images plays a vital role in urban planning. It remains challenging because of scene complexity, fluctuating road structures, and inappropriate illumination circumstances. The design of intelligent models and other machine learning (ML) approaches for road classification has yet to be further explored. In this aspect, this paper presents a metaheuristics optimization with deep autoencoder enabled road classification model (MODAE-RCM). The presented MODAE-RCM technique mainly focuses on the classification of roads into five types, namely wet, ice, rough, dry, and curvy roads. In order to accomplish this, the presented MODAE-RCM technique exploits modified fruit fly optimization (MFFO) with neural architectural search network (NASNet) for feature extraction. In order to classify roads, an interactive search algorithm (ISA) with a DAE model is used. The exploitation of metaheuristic hyperparameter optimizers helps to improve the classification results. The experimental validation of the MODAE-RCM technique was tested by employing a dataset comprising five road types. The simulation analysis highlighted the superior outcomes of the MODAE-RCM approach to other existing techniques.

Deep Learning-Based Anomaly Traffic Detection Method in Cloud Computing Environment

To address the problem of poor detection performance of existing intrusion detection methods in the environment of high-dimensional massive data with uneven class distribution, a deep learning-based anomaly traffic detection method in cloud computing environment is proposed. First, the fuzzy C -means (FCM) algorithm is introduced and is combined with the general regression neural network (GRNN) to cluster the samples to be classified in the original space by FCM. Then, the GRNN model is trained and the center point is updated using the sample closest to the FCM clustering center until a stable cluster center is obtained. The parameters in FCM-GRNN are optimized using the global optimization feature of the modified fruit fly optimization algorithm (MFOA), and the optimal spread value is found using the three-dimensional search method through an iterative search. Finally, experiments are conducted based on the KDD CUP99 dataset, and the results demonstrate that the detection rate (DR) and false alarm rate (FAR) of the proposed FCM-MFOA-GRNN method are 91% and 1.176%, respectively, which are better than those of the comparison methods. Therefore, the proposed method has good anomaly traffic detection ability.

Retracted] Urban Road Landscape Design and Digital Twin Simulation Modeling Analysis

The concept of a digital twin (DT) has lately become popular in the domains of urban planning and managed services. Densification and competing use of urban highways will result from an increase in local and foreign workers in the next years. The MFFO (modified fruit fly optimization) method for large‐scale data transfer is presented in this study. The first step is to get data from the internet. A map‐matching technique is also given in this section of preprocessing. Validated data may then be loaded into DT to reproduce processes. The results of DT are included in the landscape architecture and the server of the urban road network. The server’s data may be improved by modernized fruit fly optimization (MFFO) to enhance the transmission control protocol (TCP). Finally, the performances from the urban road design are examined and matched with existing techniques. The results are shown in the MATLAB simulation software. The results reveal that the proposed system increased the accuracy of digitalized urban road data protection for all secret information while also taking lesser time to execute than existing methods.

QoS aware web service selection using orthogonal array learning on fruit fly optimization approach

PurposeThis paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale service environment (web service repository) while maintaining Quality-of-Service (QoS), is referred to as web service selection (WSS). With the explosive growth of internet services, managing and selecting the proper services (or say web service) has become a pertinent research issue.Design/methodology/approachIn this paper, to address WSS problem, the authors propose a new modified fruit fly optimization approach, called orthogonal array-based learning in fruit fly optimizer (OL-FOA). In OL-FOA, they adopt a chaotic map to initialize the population; they add the adaptive DE/best/2mutation operator to improve the exploration capability of the fruit fly approach; and finally, to improve the efficiency of the search process (by reducing the search space), the authors use the orthogonal learning mechanism.FindingsTo test the efficiency of the proposed approach, a test suite of 2500 web services is chosen from the public repository. To establish the competitiveness of the proposed approach, it compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC). The empirical results show that the proposed approach outperforms its counterparts in terms of response time, latency, availability and reliability.Originality/valueIn this paper, the authors have developed a population-based novel approach (OL-FOA) for the QoS aware web services selection (WSS). To justify the results, the authors compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC) over the four QoS parameter response time, latency, availability and reliability. The authors found that the approach outperforms overall competitive approaches. To satisfy all objective simultaneously, the authors would like to extend this approach in the frame of multi-objective WSS optimization problem. Further, this is declared that this paper is not submitted to any other journal or under review.

A Hybrid Coordinated Design Method for Power System Stabilizer and FACTS Device Based on Synchrosqueezed Wavelet Transform and Stochastic Subspace Identification

The occurrence of low-frequency electromechanical oscillations is a major problem in the effective operation of power systems. The scrutiny of these oscillations provides substantial information about power system stability and security. In this paper, a new method is introduced based on a combination of synchrosqueezed wavelet transform and the stochastic subspace identification (SSI) algorithm to investigate the low-frequency electromechanical oscillations of large-scale power systems. Then, the estimated modes of the power system are used for the design of the power system stabilizer and the flexible alternating current transmission system (FACTS) device. In this optimization problem, the control parameters are set using a hybrid approach composed of the Prony and residual methods and the modified fruit fly optimization algorithm. The proposed mode estimation method and the controller design are simulated in MATLAB using two test case systems, namely IEEE 2-ar-ea 4-generator and New England-New York 68-bus 16-genera-tor systems. The simulation results demonstrate the high performance of the proposed method in estimation of local and interarea modes, and indicate the improvements in oscillation damping and power system stability.

An optimized combination prediction model for concrete dam deformation considering quantitative evaluation and hysteresis correction

Certain degree of deformation is natural while dam operates and evolves. Due to the impact of internal and external environment, dam deformation is highly nonlinear by nature. For dam safety, it is of great significance to analyze timely deformation monitoring data and be able to predict reliably deformation. A comprehensive review of existing deformation prediction models reveals two issues that deserves further attention: (1) each environmental influencing factor contributes differently to deformation, and (2) deformation lags behind environmental factors (e.g., water level and air temperature). In response, this study presents a combination deformation prediction model considering both quantitative evaluation of influencing factors and hysteresis correction in order to further improve estimation accuracy. In this study, the complex relationship in deformation prediction is effectively captured through support vector machine (SVM) modeling. Furthermore, a modified fruit fly optimization algorithm (MFOA) is presented for SVM hyper-parameter optimization. Also, a synthetic evaluation method and a hysteresis quantification algorithm are introduced to further enhance the MFOA-SVM-based model in regards to contribution quantification and phase correction respectively. The accuracy and validity of the proposed model is evaluated in a concrete dam case, where its performance is compared with other existing models. The simulated results indicated that the proposed nonlinear MFOA-SVM model considering both quantitative evaluation and hysteresis correction, abbreviated as SEV-MFOA-SVM, is more accurate and robust than conventional models. This novel model also provides an alternative method for predicting and analyzing dam deformation and evolution behavior of other similar hydraulic structures.

Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

Precise and steady substation project cost forecasting is of great significance to guarantee the economic construction and valid administration of electric power engineering. This paper develops a novel hybrid approach for cost forecasting based on a data inconsistency rate (DIR), a modified fruit fly optimization algorithm (MFOA) and a deep convolutional neural network (DCNN). Firstly, the DIR integrated with the MFOA is adopted for input feature selection. Simultaneously, the MFOA is utilized to realize parameter optimization in the DCNN. The effectiveness of the MFOA–DIR–DCNN has been validated by a case study that selects 128 substation projects in different regions for training and testing. The modeling results demonstrate that this established approach is better than the contrast methods with regard to forecasting accuracy and robustness. Thus, the developed technique is feasible for the cost prediction of substation projects in various voltage levels.

A novel composite neural network based method for wind and solar power forecasting in microgrids

Nowadays, wind and solar power generation have a major impact in many microgrid hybrid energy systems based on their cost and pollution. On the other hand, accurate forecasting of wind and solar power generation is very important for energy management in microgrids. Therefore, a novel prediction interval model, consisting of several sections (wavelet transform, hybrid feature selection, Group Method of Data Handling neural network, and modified multi-objective fruit fly optimization algorithm), has been developed to short-term predict wind speed and solar irradiation and to investigate the energy consumption of microgrids. The renewables prediction and the energy consumption analysis have been applied to the Favignana island microgrid, in the south of Italy, using the new proposed point forecast model (Group Method of Data Handling neural network and modified fruit fly optimization algorithm – GMDHMFOA) and a Pareto analysis. The results show that the proposed interval prediction model has a good performance in different confidence levels (95%, 90%, and 85%) to predict wind speed and solar irradiation than other already existing methods. In addition, the proposed point forecast model (GMDHMFOA) has an acceptable error and better performance than the other ones commonly used in predicting energy consumption. Lastly, the monthly energy consumption in different stations of the microgrid can be predicted by using the proposed model and provides suitable solutions for energy management of the microgrid.

A Multi-Objective Memetic Algorithm for a Fuzzy Parallel Blocking Flow Shop Scheduling Problem of Panel Block Assembly in Shipbuilding

Scheduling plays an important role in improving the efficiency of panel block assembly. Because of the characteristics of the assembly lines and the imprecise and vague temporal parameters in real-world production, the scheduling of panel block assembly on parallel lines is formulated as a fuzzy parallel blocking flow shop scheduling problem with fuzzy processing time and fuzzy due date to minimize the fuzzy makespan and maximize the average agreement index. To solve this combinational optimization problem, a multiobjective memetic algorithm (MOMA) is proposed. In the MOMA, two novel heuristics are designed to generate several promising initial solutions, and a local search method is embedded to improve the exploitation capability. The performance of the MOMA is tested on the production instances of panel block assembly in shipbuilding. Computational comparisons of the MOMA with two other well-known multi-objective evolutionary algorithms demonstrate its feasibility and effectiveness in generating optimal solutions to the bi-criterion fuzzy scheduling problem of panel block assembly. 1. Introduction The development of large ships leads to an increased demand for panel blocks. Most large shipyards have established panel block assembly lines. The scheduling of panel block assembly belongs to a class of blocking flow shop scheduling problem (BFSP) because there is no buffer between consecutive stations (machines) in an assembly line. The BFSP has received increasing attention in the literature, and most studies consider minimizing the makespan. For more than a decade, various heuristics have been proposed for the scheduling problem with a makespan criterion, including the Nawaz-Enscore-Ham (NEH) heuristic (Nawaz et al. 1983), Min-Max enumeration heuristic (Ronconi 2004), and NEH-Wang-Pan-Tasgetiren (NEH-WPT) heuristic (Wang et al. 2011). Moreover, a growing number of meta-heuristics have also been developed for this type of problem, such as the genetic algorithm (GA) (Caraffa et al. 2001), tabu search approaches (TS and TS+M) (Grabowski & Pempera 2007), iterated greedy algorithm (Ribas et al. 2011), discrete particle swarm optimization algorithm (Wang & Tang 2012), memetic algorithm (MA) (Pan et al. 2013), and modified fruit fly optimization algorithm (Han et al. 2016).

High-precision indoor three-dimensional localization scheme based on visible light communication using modified fruit fly optimization algorithm

Recently, indoor positioning system (IPS) based on visible light communication (VLC) has gradually become a hot research topic. However, many existing indoor localization schemes based on VLC only provide solutions of positioning on a two-dimensional plane. Some IPSs-based VLC that focus on three-dimensional (3-D) positioning usually contain various sensors or use complex algorithms. A high-precision indoor 3-D localization scheme based on VLC using modified fruit fly optimization algorithm (MFOA) is proposed. Since it is difficult to directly calculate the coordinate of PD receiver in 3-D positioning problem, we transform the 3-D positioning problem into a global optimization problem and design the corresponding fitness function according to the Lambertian radiation pattern, which can achieve a higher accuracy and reduce system complexity. By changing the structure of fly group in the traditional fruit fly optimization algorithm and designing an adaptive search scope, the MFOA is obtained to solve the positioning problem, which leads to a nice positioning effect. With a good convergence performance, our simulation shows an average error of 0.76 cm within 20 iterations in an indoor space of 4 m × 4 m × 6 m, and most 3-D positioning errors are <1.85 cm, which shows a good positioning performance. Therefore, our indoor localization scheme has a potential value in the actual practice of indoor localization.

Modified fruit fly optimization algorithm of logistics storage selection

From the perspective of the cost analysis of commodity marketing, the logistics cost accounts for about 30% of the final price of commodities, and the cost of storage selection accounts for about 75% of all logistics costs. This means that the management of the operation efficiency of storage is an essential part of logistics management. With four different modified fruit fly optimization algorithms, this study aimed to optimize the logistics storage selection. First, this study selected 10, 20, and 30 freight sections in a random manner; second, four fruit fly optimization algorithms were used to create some populations and fruit flies, which were taken as freight section spots; third, the algorithms were adopted to seek the logistics storage selection that costs the shortest time. According to analysis of the result, the chaos fruit fly algorithm, out of the four, was the one that took the shortest time and created the greatest effect when 10, 20, or 30 freight sections were considered. The 100 repetitious experiments also demonstrated that the chaos fruit fly algorithm cost the shortest time in the selection, and had the lowest time value variable.

Modified FOA Applied to Parameter Extraction of Flux-Gate Core

The accuracy of the magnetic core model is important to the analysis and design of the flux-gate sensor. The Jiles-Atherton model (J-A model) is the mostly used model to describe the hysteresis characteristics of the flux-gate core. But the parameters of J-A model are difficult to identify. In this paper, Fruit Fly Optimization Algorithm (FOA) is proposed to identify the parameters of the J-A model. In order to enhance the performance of the identification, a Modified Fruit Fly Optimization Algorithm (MFOA) is applied to extract the parameters of the flux-gate core. The effectiveness of MFOA is verified through five typical test functions. The influence of variation factor h on the performance of MFOA is discussed. The impact of variation factor h on parameters extraction of hysteresis loop is studied. It is shown that MFOA with appropriate selection of variation factor h will get better performance in the accuracy, stability, and simulation time compared to those of PSO and FOA.

The error prediction of inventory reconciliation during storage and transportation process based on PLS and MFOA-LSSVM

Process optimization and fault diagnosis technology, which is represented by production process monitoring, design and production condition adjustment, play an important role in the modern petroleum industry. Accurate inventory reconciliation model is the basis of process optimization and fault diagnosis. To eliminate the impact of the inventory reconciliation error caused by different metering systems, the error prediction method of inventory reconciliation during storage and transportation process based on partial least squares (PLS) and least squares support vector machine optimized by modified fruit fly optimization algorithm (MFOA-LSSVM) is proposed. The general error prediction method flow of inventory reconciliation is provided. The principles of PLS and MFOA-LSSVM are elaborated in detail. Firstly, the algorithm of PLS is used to exclude the interference of unrelated factors and extract the most relevant factors that influence the error of inventory reconciliation. Then the modified three-dimensional fruit fly optimization algorithm with diminishing steps as well as a good global search capability is adopted to select the LSSVM model parameters and build the error prediction model. Finally, the sample data were revised by using the predictive value to verify the validity of the proposed method. The experimental modeling was carried out by PLS and MFOA-LSSVM. Compared with other forecasting methods, this method not only has the advantage of faster calculations, but also can well predict the error of reconciliation.

A robust fuzzy tree method with outlier detection for combustion models and optimization

The fuzzy tree (FT) method is a new efficient modeling algorithm. To improve the performance of detecting noise and outliers for most industrial applications, this paper proposes a robust fuzzy called weighted FT (W-FT). A typical nonlinear example is used in the numerical experiments to validate the proposed W-FT. Then, the W-FT is used for building the combustion models, mainly three soft sensor models are established considering boiler efficiency, NOx and SO2 emissions by using the historical data of a circulating fluidized bed (CFB) boiler. Compared with other methods, the W-FT method exhibits more robustness, higher prediction accuracy and better generalization capability. Moreover, in basis of above soft sensor models, three types of optimization strategies are proposed to optimize the adjustable parameters by using the modified fruit fly optimization algorithm. Simulation results validate the effectiveness of the proposed optimization strategies, and further demonstrate the practicability of soft sensor models by W-FT.

MFOA-integrated modified inverted F-based adaptive array Antenna for 2.4 GHz band applications

This research has proposed a modified fruit fly optimization algorithm (MFOA)-integrated adaptive array antenna (AAA) for the 2.4–2.5 GHz WLAN system. The principal components of the array antenna system encompass four array elements, four band pass filters (BPF), four digital phase shifters, a four-way power combiner/splitter, a directional coupler, a radio frequency (RF) detector, and a microcontroller unit (MCU). In the realization of the adaptive antenna system, the modified inverted F antenna with a finite ground plane was first innovated and subsequently deployed as the element of the four-element array antenna. In the study, simulations and experiments were carried out with the four-element AAAs of two configurations, i.e. the linear and planar array configurations. The simulation and experimental results revealed that the MFOA algorithmic scheme could determine the direction of the maximum arrival signal in an efficient and accurate manner and also was capable of manipulating the radiation pattern in the desired direction. In addition, the MFOA-integrated four-element AAA is of compact size (20 mm × 35 mm × 1.8 mm) and operable in the 2.31–2.55 GHz frequency band with omnidirectional radiation pattern and a gain of 1.6 dBi. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2016.

Coordination of PSSs and SVC Damping Controller to Improve Probabilistic Small-Signal Stability of Power System With Wind Farm Integration

A modified fruit fly optimization algorithm (MFOA) combined with a probabilistic approach are proposed in this paper to coordinate and optimize the parameters of power system stabilizers (PSSs) and static VAR compensator (SVC) damping controller for improving the probabilistic small-signal stability of power systems with large-scale wind generation, taking into consideration the stochastic uncertainty of system operating conditions. It is generally accepted that there is a threat to the stability of power system with penetration of wind farm. In addition, the stochastic fluctuations of wind generation make PSSs tuning more difficult. In this paper, PSSs and SVC damping controller are employed for suppressing local and inter-area low frequency oscillation. In order to eliminate the adverse effect between PSSs and SVC damping controller, the MFOA based on the probabilistic eigenvalue is applied to coordinate and optimize their parameters. The effectiveness of the proposed approach is verified on two test systems.

Optimizing a location allocation-inventory problem in a two-echelon supply chain network: A modified fruit fly optimization algorithm

In this paper, a design of the supply chain distributer–retailer network for a seasonal multiple-product location allocation-inventory control problem in a planning horizon consisting of multiple periods is modeled. The distance between the distributers and retailers are assumed to be Euclidean and square Euclidean while retailers purchase the products from the distributers under all-unit and incremental quantity discount policies. Furthermore, the products are delivered in packets of known size of items and in case of shortage, a fraction of demand is considered backorder and a fraction lost sale. Besides, the distributers store the manufactured products in their warehouses before delivering them to the retailers since the total warehouse spaces and the total available budget are limited. Capacity constraints are also taken into account when planning inventory levels. It is considered that the distributers manufacture the products under some production limitations. The aim of the problem is to find the optimal number of packets of the products purchased by the retailers from the distributers in different periods and determine the coordinates of the distributers’ locations to minimize the total inventory cost. As the mixed integer nonlinear model of the problem is complicated to solve using exact methods, a modified fruit fly optimization algorithm (MFOA) is proposed to find the optimal solution. Due to the nonlinear nature of the original formulation and noticing that there is no benchmark available in the literature to justify and validate the results, particle swarm optimization (PSO) and simulated annealing (SA) algorithms are represented as well. Some numerical examples are generated to show the performance and application of the algorithms for both Euclidean and square Euclidean distances where the MFOA has a better performance than the PSO and SA.

Modified Fruit Fly Optimization Algorithm for Analysis of Large Antenna Array

This research paper deals with the optimization of a large antenna array for maximum directivity using a modified fruit fly optimization algorithm (MFOA) with random search of two groups of swarm and adaptive fruit fly swarm population size. The MFOA is utilized to determine three nonlinear mathematical test functions, analysis of the optimal number of elements and optimal element spacing of the large antenna array, and analysis of nonuniform amplitude of antenna array. The numerical results demonstrate that the MFOA is effective in solving all test function and electromagnetic problems. The advantages of the proposed algorithm are ease of implementation, large search range, less processing time, and reduced memory requirement.

A new type bionic global optimization: Construction and application of modified fruit fly optimization algorithm

Fruit fly optimization algorithm, which is put forward through research on the act of foraging and observing groups of fruit flies, has some merits such as simplified operation, strong robustness, easy to parallel computing, and fast convergence rate; it could solve the bottlenecks of traditional intelligent optimization algorithms on precocity and low convergence speed effectively. Fruit fly optimization algorithm is applied to almost all the numerical optimization problems and is very useful in engineering applications. When the design variable is negative, traditional fruit fly optimization algorithm is not qualified for the extraordinarily slow convergence rate during the late stage of calculation and easy to be trapped in local optimum. Because of the defects of classical fruit fly optimization algorithm, a new coding method of the process of optimization is improved by this article, so the design variables could be searched toward the direction. In addition, a novel bionic global optimization—fruit fly optimization algorithm of learning—is proposed by introducing the concept of “study.” This article tries to apply fruit fly optimization algorithm of learning to compare calculations; therefore, four classical test functions and two engineering problems are performed. It turned out that not only does fruit fly optimization algorithm of learning inherit the advantages of fruit fly optimization algorithm, but has a strong learning ability. The introduction of “study” ability into fruit fly optimization algorithm notably improves the efficiency and capability of optimization; it has characteristics of fast convergence rate and fast speed of approaching the global optimum solutions. Fruit fly optimization algorithm of learning has the ability to solve practical problems, and its engineering prospect is promising.

Mixed modified fruit fly optimization algorithm with general regression neural network to build oil and gold prices forecasting model

Purpose – When facing a clouded global economy, many countries would increase their gold reserves. On the other hand, oil supply and demand depends on the political and economic situations of oil producing countries and their production technologies. Both oil and gold reserve play important roles in the economic development of a country. The paper aims to discuss this issue. Design/methodology/approach – This paper uses the historical data of oil and gold prices as research data, and uses the historical price tendency charts of oil and gold, as well as cluster analysis, to discuss the correlation between the historical data of oil and gold prices. By referring to the technical index equation of stocks, the technical indices of oil and gold prices are calculated as the independent variable and the closing price as the dependent variable of the forecasting model. Findings – The findings indicate that there is no obvious correlation between the price tendencies of oil and gold. According to five evaluating indicators, the MFOAGRNN forecast model has better forecast ability than the other three forecasting models. Originality/value – This paper explored the correlation between oil and gold prices, and built oil and gold prices forecasting models. In addition, this paper proposes a modified FOA (MFOA), where an escape parameter Δ is added to Si. The findings showed that the forecasting model that combines MFOA and GRNN has the best ability to forecast the closing price of oil and gold.