Shuffled Frog Research Articles

Optimal sizing of distributed generation units and shunt capacitors in the distribution system considering uncertainty resources by the modified evolutionary algorithm

Distributed generation units (DGUs) as auxiliary sources of power generation can play an effective role in meeting the load consumption of the distribution network, also have positive effects such as reducing loss and improving voltage. Moreover, capacitors by reactive power compensation produce positive effects similar to DGUs in the distribution networks. The idea of joint operation of DGUs and shunt capacitors (SCs) in the presence of demand response program (DRP) to derive maximum benefits from their installation is proposed in this paper. The time of use (TOU) mechanism is used as one of the demand response programs (DRPs) to alter the consumption pattern of subscribers and improve the performance of the distribution system. Objective functions include minimization of energy loss, operational cost, and energy not supplied (ENS). In general, the problem of determining the optimal capacity of DGUs and SCs is complex due to the demand variation. Also, considering the effect of uncertainty sources complicate the optimization problem. Hence, a modified shuffled frog leaping algorithm (MSFLA) is proposed to overcome the complexities of this problem. The proposed approach is tested on two 95, and 136-node test networks, and the results are compared with other evolutionary algorithms. According to the obtained results, after using the proposed approach in determining the optimal capacity of DGUs and SCs in the first system, the amount of energy loss, operational cost and ENS dropped by 11, 25.5 and 5% compared to baseline values. After applying the TOU mechanism in allocation of DGUs and SCs simultaneously in the second system by proposed method, the values obtained for the mentioned objectives reduced by 29, 65 and 7% compared to initial values.

A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking.

This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate.

A new MPPT design using variable step size perturb and observe method for PV system under partially shaded conditions by modified shuffled frog leaping algorithm- SMC controller

Renewable energies, particularly solar photovoltaic (PV) panels have been largely integrated into power systems, mainly due to the fact that this technology is environmentally friendly and available almost everywhere. However, these systems have some challenges regarding their efficiency. They should always follow the maximum power point (MPP) to deliver the highest power. To this end, maximum power point tracking methods, known as MPPT algorithms, have already been proposed to overcome this problem. The MPPT techniques, commonly used, take into consideration unchanged climate conditions. In this respect, a novel framework is developed in this paper for the MPPT algorithm based on a sliding mode controller (SMC) applicable to PV panels with partial shading conditions (PSC) and uniform conditions. This model employs the modified shuffled frog leaping algorithm (MSFLA) to derive the desired values of the parameters of the controller that utilizes the variable step-size perturb and observe (P&O). The performance of the developed framework is based on using the desired values of the SMC for the variable step-size P&O of the MPPT when the system operates in the grid-connected state. The presented framework is abbreviated MSFLA-SMC. This system shows precise tracking under changing weather conditions and it performs better compared to conventional techniques. The developed control system is associated with desired dynamic response and power flow between the PV system and the grid.

HYBRID SHUFFLED FROG LEAPING ALGORITHM WITH PROBABILITY DISPERSAL METHOD FOR TUMOR DETECTION IN 3D MRI BRAINTUMOR IMAGES

In the medical image study, the brain tumor classification using MRIs is difficult due to the brain’s complicated structure and the high variance in tumor tissues’ position. So, the requirement for useful and specific tumor identification methods is developing for medical recognition and regular medical applications. The conventional brain tumor identification performs anatomical knowledge of irregular tissues in the brain, helping the doctor design approach. The research proposes several techniques for brain tumor identification. This work aims to present brain tumor identification methods based on evolutional intelligence and segmentation. Unusual areas in the brain are identified by using the Expectation-Maximization (EM) algorithm. For segmenting the 3D brain MRI data, this work presents a novel hybrid optimization meta-heuristic called the Shuffled Frog Leaping Algorithm (SFLA) with probability dispersal (i.e., SFLA - Stochastic Diffusion Search (SDS)). The efficacy of the suggested 3D SFLA probability dispersal EM in enhancing the performance of the 3D SFLA tabu EM has been proven by empirical outcomes.

An energy‐efficient non‐uniform clustering routing protocol based on improved shuffled frog leaping algorithm for wireless sensor networks

Wireless sensor networks have been used widely in environmental tracking and monitoring. Nevertheless, WSNs are facing a number of challenges, such as unreasonable cluster head selection and energy-hole problems while nodes have unbalanced energy consumption. Thus, the paper aims to propose an energy-efficient non-uniform clustering routing protocol to enhance nodes energy efficiency and balance the energy consumption in WSNs. In addition, a non-uniform clustering network partition is introduce to reduce the probability of energy hole occurrences and optimize cluster heads dynamical selection method to suggest an improved shuffled frog leaping algorithm. Ultimately, the simulation experiment has demonstrated a 20% gain of energy efficiency to extend network lifetime by the proposed protocol and enhanced algorithm.

An Improved SFLA-Kmeans Algorithm Based on Approximate Backbone and Its Application in Retinal Fundus Image

In order to improve the global search ability of K-means algorithm and the clustering effect, a K-means method based on the approximate backbone and the shuffled frog leaping algorithm was proposed. Firstly, the classic iterative formula of the K-means algorithm is replaced by the classic shuffled frog leaping algorithm to obtain better clustering results. Secondly, the K-means algorithm based on the approximate backbone and the shuffled frog leaping algorithm is used for the obtained clustering results. Instead of searching for cluster centers, the cluster division is directly modified. Finally, the experimental results on the UCI dataset show that, the running time of the improved clustering algorithm is shorter than that based on the shuffled frog leaping algorithm only, and clustering results obtained by using the improved clustering algorithm are better than those of other algorithms. In addition, the paper uses the improved clustering algorithm to preprocess medical fundus images to optimize the effect of vascular cutting.

Control Strategy for a Quadrotor Based on a Memetic Shuffled Frog Leaping Algorithm

This work presents a memetic Shuffled Frog Leaping Algorithm (SFLA) based tuning approach of an Integral Sliding Mode Controller (ISMC) for a quadrotor type of Unmanned Aerial Vehicles (UAV). Based on the Newton–Euler formalism, a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes. Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law, which are usually selected by repetitive and time-consuming trials-errors based procedures, a constrained optimization problem is formulated for the systematically tuning of these unknown variables. Under time-domain operating constraints, such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation- and swarm intelligence-based algorithms such as the Crow Search Algorithm (CSA), Fractional Particle Swarm Optimization Memetic Algorithm (FPSOMA), Ant Bee Colony (ABC) and Harmony Search Algorithm (HSA). Numerical experiments are carried out for various sets of algorithms’ parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization. Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence. Demonstrative results verified that the proposed metaheuristics-based approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.

Способы адаптации алгоритма прыгающих лягушек к бинарному пространству поиска при решении задачи отбора признаков

The feature selection is an important step in constructing any classifier. Binary versions of metaheuristic optimization algorithms are often used for selection. However, many metaheuristics are originally created to work in the continuous search space, so they need to be specially adapted to the binary space. In this paper, the authors propose fifteen ways to binarize the Shuffled frog leaping algorithm based on the following methods: modified algebraic operations, merge operation, and transformation functions. The efficiency of the binary algorithm was tested in the problem of feature selection for fuzzy classifiers on data sets from the KEEL repository. The results show that all the described methods of binarization allow reducing the features, while increasing the overall accuracy of classification.

Optimal day-ahead scheduling of microgrid with hybrid electric vehicles using MSFLA algorithm considering control strategies

Microgrids (MGs) have turned into vital components of the modern power system with the capability of efficiently accommodating renewable energies and electric vehicles (EVs) with high flexibility. MGs can contribute to mitigating the operating cost and environmental emissions of power systems. Accordingly, the optimal operation of such systems is of very high significance. In this relation, the problem of optimal day-ahead scheduling of MGs is studied in this paper in the presence of renewable power generation, EVs, and storage systems. The problem is modeled as a scenario-based stochastic optimization problem, characterized using the Monte-Carlo simulation (MCS) method. The developed framework includes one objective function, defined as the total operating cost minimization and the presented single-objective optimization problem is tackled using an effective optimization technique, named “modified shuffled frog leaping algorithm (MSFLA)”. The suggested optimization framework takes into consideration various charging/discharging patterns of EVs. Finally, the problem is simulated on a test MG and the obtained results are compared to those derived by other algorithms to verify the performance of the MSFLA algorithm.

A Novel Hybrid Intrusion Detection System (IDS) for the Detection of Internet of Things (IoT) Network Attacks

Nowadays, IoT has been widely used in different applications to improve the quality of life. However, the IoT becomes increasingly an ideal target for unauthorized attacks due to its large number of objects, openness, and distributed nature. Therefore, to maintain the security of IoT systems, there is a need for an efficient Intrusion Detection System (IDS). IDS implements detectors that continuously monitor the network traffic. There are various IDs methods proposed in the literature for IoT security. However, the existing methods had the disadvantages in terms of detection accuracy and time overhead. To enhance the IDS detection accuracy and reduces the required time, this paper proposes a hybrid IDS system where a pre-processing phase is utilized to reduce the required time and feature selection as well as the classification is done in a separate stage. The feature selection process is done by using the Enhanced Shuffled Frog Leaping (ESFL) algorithm and the selected features are classified using Light Convolutional Neural Network with Gated Recurrent Neural Network (LCNN-GRNN) algorithm. This two-stage method is compared to up-to-date methods used for intrusion detection and it over performs them in terms of accuracy and running time due to the light processing required by the proposed method.

An improve feature selection algorithm for defect detection of glass bottles

Defect detection is an effective technology to guarantee the quality of the products. In this paper, the defect detection of glass bottles is taken as a classification problem. Features abstracted from the knocking sound signals of the glass bottles are used as the input of the classifier. Because the detection of bottles usually should be done in real time, feature selection which could reduce the dimensionality of data and simplify the learned model has become an important technology in this work. Based on our previous proposed algorithm named Shuffled Frog Leaping Algorithm-Improved Minimal Redundancy Maximal Relevance(SFLA-ImRMR), this paper focuses on improving the classification performance and proposes an improved feature selection algorithm. In the proposed algorithm, feature selection is combined with the training of the classifier, the wrapper approach is used to evaluate the selected features, Shuffled Frog Leaping Algorithm(SFLA) is used as the search algorithm, and BP Neural Network(BPNN) is used as the classifier. The proposed algorithm is named SFLA-ImRMR-BP and tested in the data sets built by the knocking sound signals of the glass bottles. Compared with SFLA-ImRMR and some other feature selection algorithms based on Evolutionary Computation(EC), features selected by SFLA-ImRMR-BP can achieve the highest classification performance.

A day-ahead economic scheduling of microgrids equipped with plug-in hybrid electric vehicles using modified shuffled frog leaping algorithm

With the ever-increasing energy demand for energy all across the world and also, the increasing concern on environmental issues due to using fossil fuels mostly in power systems, some appropriate alternatives should be used to solve the issue. In this respect, renewable energy sources (RESs) with almost zero pollution have been turned into the first choice for supplying the required energy. In this study, to achieve the minimum total cost of the grid, a novel problem formulation is proposed to reduce the reliability costs. Simultaneously, the transportation sector has been replacing the current conventional fossil-fuel vehicles with electrified ones, where plug-in electric vehicles (PEVs) as well as plug-in hybrid electric vehicles (PHEV) have captured the attention and there is an increasing rate in using electric vehicles (EVs). These vehicles are capable of connecting to the electrical grid to absorb/deliver energy from/to the grid through the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies. On the other hand, a new concept in power systems, namely microgrid (MG) has been introduced to facilitate the integration of RESs and make the most of EVs’ capabilities using the smart infrastructure. In fact, the vehicle to grid (V2G) capability is used to decrease the operating cost to achieve a proper opportunity to accommodate PEVs in the network. The output power produced by RESs is volatile, which is more obvious in wind energy and solar power. Thus, the resource management issue of MGs would be of very high significance. In this regard, this paper proposes an efficient optimization framework for the optimal day-ahead energy management of MGs, including PEVs and RESs, utilizing an effective stochastic programming method, i.e. unscented transformation (UT). It is significant to note that the problem has tackled as a single-objective stochastic optimization problem, while the objective has been defined as minimizing the total cost of operation, The presented stochastic optimization problem is then solved by employing a nature-inspired efficient method, named “modified shuffled frog leaping algorithm (MSFLA)” and the obtained results are compared to those reported by other methods to verify its performance.

Optimal energy management strategy for a renewable‐based microgrid considering sizing of battery energy storage with control policies

SummaryMicrogrids (MGs) are known as suitable options to accommodatethe high penetration of renewable energies, like solar and wind. MGs have provided the requirements of controlling and adjusting these sources. In addition, batteries are becoming indispensable components of MGs because of their capabilities in addressing the renewable energies' power output intermittency. In MGs, the problem of smart energymanagement along with battery sizing has been introduced as the necessity to ensure the efficient use of renewable sources and decrease traditional fossil‐fuel‐based generation technology penetration level in power systems. Accordingly, a novel method is presented in this paper to effectively address the above‐mentioned requirements, utilizing the modified shuffled frog leaping algorithm (MSFLA), applied to different case studies. The results, obtained from the numerical simulation, are compared to several well‐established optimization approaches to verify the performance of MSFLA. In terms of computational efficiency and quality of the obtained solution, the MSFLA has demonstrated promising outcomes, together with superior performance in comparison with other algorithms. The results show that the presented framework, including the battery sizing, would be very beneficial to minimize the operating cost of MGs.

Comprehensive Analysis of PAPR Reduction in OFDM, UFMC and Signal Scrambling UFMC Technique using Shuffled Frog Leaping Algorithm

Universal Filter Multi-Carrier (UFMC) is one of the most recommended waveform designs for the next-generation wireless communication system. UFMC provides high spectral efficiency, better sub-carrier separation with less PAPR when compared to OFDM. The major drawback of UFMC is that it suffers from a significant peak to average power ratio similar to multi-carrier communication techniques like Orthogonal Frequency Division Multiplexing (OFDM). The research gap is addressed by proposing the signal scrambling technique to reduce the Peak Average Power Ratio(PAPR) in UFMC. A Shuffled Frog Leaping Algorithm (SFLA) is used as an optimizer to verify the entire search space for optimal phase vectors within the time constraints to produce the best-modified UFMC signal with less PAPR. An analogy between OFDM, UFMC and signal-scrambling UFMC is performed, and it is observed that the signal-scrambled UFMC produces better results than general UFMC and OFDM in terms of PAPR.

A Modified Shuffled Frog Leaping Algorithm for the Topology Optimization of Electromagnet Devices

The memetic algorithms which employ population information spreading mechanism have shown great potentials in solving complex three-dimensional black-box problems. In this paper, a newly developed memetic meta-heuristic optimization method, known as shuffled frog leaping algorithm (SFLA), is modified and applied to topology optimization of electromagnetic problems. Compared to the conventional SFLA, the proposed algorithm has an extra local search step, which allows it to escape from the local optimum, and hence avoid the problem of premature convergence to continue its search for more accurate results. To validate the performance of the proposed method, it was applied to solving the topology optimization of an interior permanent magnet motor. Two other EAs, namely the conventional SFLA and local-search genetic algorithm, were applied to study the same problem and their performances were compared with that of the proposed algorithm. The results indicate that the proposed algorithm has the best trade-off between the results of objective values and optimization time, and hence is more efficient in topology optimization of electromagnetic devices.

Optimal local dimming based on an improved greedy algorithm

As a new technology appeared in recent years, the local dimming can effectively reduce the power consumption of a display system and improve its display effect. A suitable local dimming algorithm should have efficient performance and can make the displayed images have higher visual quality. However, most of the existing local dimming methods can not have both of the above advantages. In this paper, the local dimming is taken as an optimization problem. On the basis of our previous work which focuses on reducing the image distortion and power consumption, image contrast ratio which is another important factor of visual quality is also considered. To improve the running efficiency of local dimming, the Greedy Algorithm (GRA) which is one of the simplest heuristic algorithms is used to design the local dimming algorithm. In order to improve the global optimization ability of the GRA, an Improved Greedy Algorithm(IGRA) based on the strategies of Taking out-Putting in and variable search step size is proposed. Experienced in four different types of images and compared with five parameter-based algorithms, the IGRA can obtain a higher visual quality under the same or lower power consumption. It is also proved that the IGRA has more powerful search ability and higher running efficiency by the comparisons with the Improved Shuffled Frog Leaping Algorithm (ISFLA) proposed in our previous work, and two recent algorithms including the Modified Genetic Algorithm (MGA) and the Improved Particle Swarm Optimization (IPSO).

K-coverage m-connected node placement using shuffled frog leaping: Nelder–Mead algorithm in WSN

In wireless sensor network (WSN) target-based coverage plays a vital role in forwarding the information from source to destination via multiple sensors by covering the given targets in location specific wise. The coverage of targets and connectivity among the sensors are the two most addressable issues that are to be considered for effective data transmission. In this paper, a mathematical model called Nelder–Mead method is imposed with shuffled frog leaping algorithm for improvised local search to deploy the sensors to cover the given targets without violating the constraints in coverage as well as connectivity. The proposed algorithm is evaluated with standard performance metrics and compared with the existing algorithms and shows the significance of the proposed algorithm.

Analysis and enhancement of PV efficiency with hybrid MSFLA–FLC MPPT method under different environmental conditions

Recently, the tendency towards employing a combined system for generating electricity has significantly increased. In this respect, the combination of a photovoltaic (PV) system and an energy storage system has captured the attention. In this respect, this paper develops a control system for a combined energy system, comprised of a PV and a battery in the grid-connected mode. There are several research works being carried out to exploit the highest power from PV systems, due to their high investment costs. An interesting and effective approach is utilizing a maximum power point tracking so-called “MPPT” algorithm for the PV panel to raise efficiency. This paper proposes the fuzzy logic controller (FLC) and the modified shuffled frog leaping algorithm (MSFLA) to optimally tune the parameters of the controller and follow the maximum power point (MPP). In this respect, an efficient theoretical framework has been developed for the MPPT algorithm to assess its performance under various conditions. Besides, a comprehensive comparison has been made to evaluate the model in different weather conditions, the method developed in this paper is associated with 99% efficiency while other methods result in 95%–97% efficiency with low speed at reaching the MPP that highlight the superior performance of the presented technique and other methods. Accordingly, the techniques studied are ranked as follows based on their performance: MSFLA-FLC > FLC > IC > P&O while their response times are 0.13s, 0.18s, 0.26s and 0.32s,respectively.

Reduce energy consumption in sensors using a smartphone, smartwatch, and the use of SFLA algorithms (REC-SSS)

Wireless body area networks are a technology for remote medical care. Because of the limited energy of the sensors, one of the problems of long-distance medical care is the high energy consumption when sending information of sensors to the sink. Choosing the proper route when sending information to the sink will reduce energy consumption and increase network lifetime. The current paper used the shuffled frog leaping algorithm (SFLA) to find an appropriate route that can send information of sensors to the lowest energy to the sink or coordinator. Also, to prevent data traffic in the sink, the sensors are divided into two groups of four. With the advent of the Internet of things and increasing use of them among people, the use of this technology has attracted the attention of remote medical care. Smartphones and smartwatches can measure information from different parts of the body, such as the heart, stroll, and glucose, based on their built-in sensors such as accelerometers, gyroscopes, and advanced cameras. These two smart devices are used in three different roles (coordinator, sink, and sensor) to improve energy efficiency in remote medical care. The paper is shown an appropriate path to reduce energy consumption in sensors using a smartphone, smartwatch, and the use of SFLA algorithms (REC-SSS). The simulation results show that the network stability increased by 12.5%,132%, and 3.5% compared to SIMPLE, M-ATTEMPT, and EERP, respectively. Also, in the proposed schema, the lifetime of the network is increased by 26% over SIMPLE, M-ATTEMPT, and EERP.

An enhanced cost-aware mapping algorithm based on improved shuffled frog leaping in network on chips

Network on chip (NoC) has been of great interest in recent years. However, according to the recent studies, high communication cost has been raised as the one most important challenges in mapping process in NoC. In order to address these issues, this research takes the advantages of the improved shuffled frog leaping based on two-dimensional torus networks in figure of an enhanced algorithm. In other words, using the proposed mapping category features, increase in the number of nodes has very little effect on the provided performance by the proposed algorithm, because it first improves the write cost of each cluster with less instead of high communication cost. Moreover, nodes having greater relevance in the graph of application are often adjacent to each other. Therefore, while the communication cost is reduced, the search space mapping gets larger which leads to easier and less costly access to the optimal mapping. In fact, between two nodes with higher weight, it is less distance to write in the network substrate. This reduction reduces the distance between the two nodes in the end, reducing the cost of communication. Simulation results show that the communication cost is improved by 3.62, 0.90, 0.72 and 1.35% compared to PSMAP algorithm for the 263dec mp3dec graph, Lmap algorithm for the 263enc mp3dec graph, Lmap algorithm for the MWD graph and PSMAP algorithm for MPEG-4 graph, respectively.