Shuffled Frog Leaping Algorithm Algorithm Research Articles

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

A combined algorithm for reconfiguring and placement of energy storage systems, electric vehicles, and distributed generation (DG) in a distribution network is presented in this paper. The impact of this technique on increasing the network’s resilience during critical periods is investigated, as well as improvements in the network’s technical and economic characteristics. The three objective functions in this regard are the network losses, voltage profile, and running costs, which include the costs of purchasing electrical energy from the upstream network and the devices used, as well as the cost of load shedding. In addition, the impacts of the presence of DG on power flow and the modeling of the problem’s objective function are examined. To solve the problem of reconfiguration and placement of a multi-objective distribution feeder, a genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) hybrid algorithm is used. The proposed GA–SFLA algorithm is used to solve the problem with changes in its structure and its combination in three stages. Finally, the proposed method is implemented on the 33-bus distribution network. The simulation results show that the proposed method has an effective performance in improving the considered objective functions and by establishing a suitable fit between the different objective functions based on the three-dimensional Pareto front. Moreover, it introduced a more optimized architecture with lower loss, lower operating costs, and greater reliability compared to other optimization algorithms.

Resource Allocation Based on SFLA Algorithm for D2D Multicast Communications

Multicast device-to-device (D2D) communication technology is considered as one of the new technologies in the fifth generation (5G) networks that directly addresses the need for content sharing among internet users. In fact, when direct communication is available between devices, the spectral efficiency is improved by reusing the licensed cellular spectrum. The current studies show that D2D communication increases network capacity and reduces latency. In order to achieve the alternate capabilities, coordination is required to implement interference management. We considered subcarrier allocation for the uplink, in addition to the power control that takes place on the underlay network. The completed data rate in single multicast communication is significantly reduced and limited by nodes with lower channel quality. In this paper, we used Shuffled Frog Leaping Algorithm (SFLA) for resource allocation (RA) in D2D multicast communications. We compared the results of the SFLA algorithm with the Firefly Algorithm (FA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO); in terms of D2D user throughput, Cellular User (CU) throughput, network average throughput, network interference and signal interference noise ratio (SINR) target. The simulation results show that SFLA clearly outperforms other algorithms in terms of data rate under the high pressure of infeasibility.

Estimation of actual evapotranspiration: A novel hybrid method based on remote sensing and artificial intelligence

Actual evapotranspiration (AET) is one of the decisive factors controlling the water balance at the catchment level, particularly in arid and semi-arid regions, but measured data for which are generally unavailable. In this study, performance of a base artificial intelligence (AI) model, adaptive neuro-fuzzy inference system (ANFIS), and its hybrids with two bio-inspired optimization algorithms, namely shuffled frog leaping algorithm (SFLA) and grey wolf optimization (GWO), in estimating monthly AET was evaluated over 2001–2010 across Neishaboor watershed in Iran. The inputs of these models were categorized into three groups including meteorological, remotely sensed, and hybrid-based predictors, and defined in the form of 8 different scenarios. Net radiation (Rn), land surface temperature (LST), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and soil wetness deficit index (SWDI) were the remotely sensed predictors, computed using MODIS satellite images on the monthly scale for the study area. The results showed that the SWDI predictor has played a significant role in improving the accuracy of AET estimation, with the highest error reduction (12.5, 17 and 26.5% for ANFIS, ANFIS-SFLA, and ANFIS-GWO, respectively) obtained under scenarios including SWDI compared to corresponding scenarios excluding this predictor. In testing set, the three aforementioned models exhibited their best performance under Scenario 8 (RMSE = 11.93, NSE = 0.69, RRMSE = 0.37), Scenario 4 (RMSE = 11.06, NSE = 0.74, RRMSE = 0.37) and Scenario 4 (RMSE = 10.9, NSE = 0.76, RRMSE = 0.36), respectively. Coupling the SFLA and GWO optimization algorithms to the base model improved the accuracy of AET estimation, with the maximum error reduction for the two algorithms being about 12% (Scenarios 2 and 4) and 14% (Scenario 4), respectively. Examining the performance of the best scenarios of the three models in three intervals including the first, middle, and last third of measured AET values showed that all models were the most accurate in the first third interval. The results also indicated that all models have had higher accuracies in the first and middle third intervals of under-estimation set and the last interval of over-estimation set.

A Hybrid Shuffled Frog Leaping Algorithm and Its Performance Assessment in Multi-Dimensional Symmetric Function

Ensemble learning of swarm intelligence evolutionary algorithm of artificial neural network (ANN) is one of the core research directions in the field of artificial intelligence (AI). As a representative member of swarm intelligence evolutionary algorithm, shuffled frog leaping algorithm (SFLA) has the advantages of simple structure, easy implementation, short operation time, and strong global optimization ability. However, SFLA is susceptible to fall into local optimas in the face of complex and multi-dimensional symmetric function optimization, which leads to the decline of convergence accuracy. This paper proposes an improved shuffled frog leaping algorithm of threshold oscillation based on simulated annealing (SA-TO-SFLA). In this algorithm, the threshold oscillation strategy and simulated annealing strategy are introduced into the SFLA, which makes the local search behavior more diversified and the ability to escape from the local optimas stronger. By using multi-dimensional symmetric function such as drop-wave function, Schaffer function N.2, Rastrigin function, and Griewank function, two groups (i: SFLA, SA-SFLA, TO-SFLA, and SA-TO-SFLA; ii: SFLA, ISFLA, MSFLA, DSFLA, and SA-TO-SFLA) of comparative experiments are designed to analyze the convergence accuracy and convergence time. The results show that the threshold oscillation strategy has strong robustness. Moreover, compared with SFLA, the convergence accuracy of SA-TO-SFLA algorithm is significantly improved, and the median of convergence time is greatly reduced as a whole. The convergence accuracy of SFLA algorithm on these four test functions are 90%, 100%, 78%, and 92.5%, respectively, and the median of convergence time is 63.67 s, 59.71 s, 12.93 s, and 8.74 s, respectively; The convergence accuracy of SA-TO-SFLA algorithm on these four test functions is 99%, 100%, 100%, and 97.5%, respectively, and the median of convergence time is 48.64 s, 32.07 s, 24.06 s, and 3.04 s, respectively.

Optimal schedule recovery for the aircraft gate assignment with constrained resources

Efficient ground handling at airports contributes significantly to the performance of the entire air transport network. In this network, airports are coupled by flights that depend on passenger and crew connections, effective local airport operations, and efficient ground handling resource management. In addition, airport stakeholders must consider different time scales (look-ahead times), process estimates, and both bounded and multiple-dependent solution spaces for their decision-making processes. In this context, we aim to solve the NP-hard problem of combined aircraft turnaround time and airport gate allocation optimization. We developed a hybrid meta-heuristic algorithm based on the Shuffled Frog-Leaping Algorithm (SFLA) and the Grasshopper Optimization Algorithm (GOA). Our hybrid SFLA-GOA approach overcomes the drawback of each algorithm and achieves a preferred solution at a reduced computational cost. We test our approach with a scenario derived from Frankfurt Airport operations, including gate positions, terminal layout, and flight plan information. In all defined problem sizes, the hybrid SFLA-GOA algorithm outperforms an independent implementation of the SFLA algorithm. Also, the SFLA-GOA algorithm provides an improved solution by at least nearly 12% up to 40% over the standard mixed-integer programming solvers.

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.

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.

A hybrid shuffled frog‐leaping and pattern search algorithm for load frequency controller design of a two‐area system composing of PV grid and thermal generator

AbstractThe shuffled frog‐leaping algorithm (SFLA) is a nature‐inspired metaheuristic swarm‐based optimization algorithm, which mimics the social behavior of memetics. The SFLA comprises an arrangement of communicating virtual population of frogs divided into various memeplexes. Many attempts have been made to find the variant of the SFLA that performs better on a variety of optimization tasks by making the original SFLA more complex. This paper proposes the hybrid approach by combining the SFLA algorithm with a pattern search algorithm, which improves the original technique named as the hybrid shuffled frog‐leaping and pattern search algorithm (hSFLA‐PS). The superiority of the proposed hybrid algorithm over the original SFLA in terms of implementation time and solution quality is compared by taking several benchmark test functions. In the next step, the real application of the proposed hybrid approach in the engineering field is done by designing a proportional‐integral‐derivative (PID) controller for frequency regulation of a two‐area power system that is composed of a photovoltaic (PV) grid and a thermal generator. It is observed that the hSFLA‐PS–based PID controller is more effective for the load frequency control compared with conventional controllers tuned with genetic algorithm (GA) and firefly algorithm (FA).

SFLA‐based heuristic method to generate software structural test data

AbstractSoftware testing is one of the significant stages in software development life cycle which is a costly and time‐consuming task. Automatic tests data generation is one of the traditional techniques to reduce the cost and time spent in software testing. Different evolutionary algorithms have been proposed to generate test data which cover target paths in a software program. In this paper, shuffled frog leaping algorithm (SFLA) is proposed to generate structural test data. The proposed SFLA algorithm is characterized by high convergence speed and simple implementation. In the proposed SFLA, branch coverage is used as the fitness function to generate effective test data. For comparing the performance of the proposed SFLA with genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC), seven benchmark programs were used. The results indicated that the proposed SFLA has an average of 99.99% for branch coverage, average 99.97% for success rate, and 2.03 for the average number of generation for covering all branches.

A Memetic based Approach for Routing and Wavelength Assignment in Optical Transmission Systems

In optical networks, Routing and Wavelength Assignment (RWA) problem is one of the major optimization problems. This problem can be solved by different algorithms such as Genetic Algorithm (GA), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), etc. Shuffled Frog Leaping Algorithm (SFLA) is implemented in the proposed work, to solve the RWA problem in long-haul optical networks. The goal is to use minimum number of wavelengths and to reduce the number of connection request rejections. Cost, number of wavelengths, hop count and blocking probability are the performance metrics considered in the analysis. Various wavelength assignment methods such as first fit, random, round robin, wavelength ordering and Four Wave Mixing (FWM) priority based wavelength assignment are used in the analysis using SFLA. Number of wavelengths, hop count, cost and setup time are included in the fitness function. The SFLA algorithm proposed, has been analyzed for different network loads and compared with the performance of genetic algorithm.

An outsourcing service selection method using ANN and SFLA algorithms for cement equipment manufacturing enterprises in cloud manufacturing

In cloud manufacturing (CMfg) environments, an increasing number of manufacturing enterprises outsource manufacturing activities to subcontractors that are more professional to focus on the development of their core business. Volume, diversity, and variety are the typical characteristics of outsourcing services for cement equipment manufacturing enterprises (CEMEs). To address the new problem of service discovery and combinatorial optimization of outsourcing resources (COOR), a novel heuristic approach is investigated in this paper. First, a clustering and searching model of a web outsourcing service based on Ontology Web Language for Services (OWL-S) and an artificial neural network (ANN) is established. Then, an improved shuffled frog leaping algorithm (SFLA) is developed to solve the COOR problem. Finally, an investigation and comparative experiments based on a group of cement equipment manufacturing companies is presented. The experimental results show that the proposed method is preferable and is more efficient for solving large-scale problems in a CMfg environment.

A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration

Distribution Feeder Reconfiguration (DFR) is an important technique to improve the performance of distribution networks. The common objectives considered in the DFR problem are power loss and voltage deviation which are important objectives for traditional distribution systems. Security issues cause by Distributed Generations (DGs) in modern distribution systems which can potentially jeopardize power system security has almost neglected in power system operation problem. Toward this end, this study considers the power loss, Voltage Stability Index (VSI), and number of switching as objective functions which can satisfy both operation and security expectations. The Backward-Forward Sweep (BFS) method known for easy convergence has been employed for power flow calculations. Because of the increase in DG penetration in distributed systems, the impacts of these units are investigated. A powerful optimization algorithm based on hybridization of Shuffled Frog Leaping Algorithm (SFLA) and Particle Swarm Optimization (PSO) is proposed to solve the proposed problem. The proposed algorithm is a combination of strong mutation operator, original SFLA and original PSO algorithms which has high population diversity and search ability. The proposed algorithm has been applied to a complex multimodal benchmark function and also two different distribution networks including 33- and 95-bus test systems.

An Efficient Hybrid Algorithm Based on HS and SFLA

When used for optimizing complex functions, harmony search (HS) and shuffled frog leaping algorithm (SFLA) algorithm tend to easily get trapped into local optima and result in low convergence precision. To overcome such shortcomings, a hybrid mechanism of selective search by combining HS algorithm and SFLA algorithm is as well proposed. An HS-SFLA algorithm is designed by taking the advantages of HS and SFLA algorithms. The hybrid algorithm of HS-SFLA is adopted for dealing with complex function optimization problems, the experimental results show that HS-SFLA outperforms other state-of-the-art intelligence algorithms significantly in terms of global search ability, convergence speed and robustness on 80% of the benchmark functions tested. The HS-SFLA algorithm could directly be applied to all kinds of continuous optimization problems in the real world.

Research and Application of Parallel Normal Cloud Mutation Shuffled Frog Leaping Algorithm in Cascade Reservoirs Optimal Operation

In order to improve the premature convergence problem of traditional shuffled frog leaping algorithm (SFLA), this paper proposed a normal cloud mutation shuffled frog leaping algorithm (NCM-SFLA) by mixing the cloud model algorithm (NCM) with SFLA algorithm, NCM is used to overcome the shortage of SFLA which is easy to fall into local optimal solution. The proposed NCM-SFLA has a good parallel characteristic, and the parallel computing can be implemented easily in multi core environment. In case study, this paper takes the Li Xianjiang cascade reservoirs in China as an instance to solve the cascade reservoirs operation optimization problem by the proposed NCM-SFLA. The results show that, compared with the Multi- dimensional Dynamic Programming (MDP), NCM-SFLA has the better global search ability and faster convergence speed, and the corresponding parallel computing can effectively shorten the run-time of NCM-SFLA. Therefore, the feasibility and rationality of the proposed NCM-SFLA and its parallel computing are effectively proved by the case study results.

Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems

The shuffled frog-leaping algorithm (SFLA) is a relatively new meta-heuristic optimization algorithm that can be applied to a wide range of problems. After analyzing the weakness of traditional SFLA, this paper presents an enhanced shuffled frog-leaping algorithm (MS-SFLA) for solving numerical function optimization problems. As the first extension, a new population initialization scheme based on chaotic opposition-based learning is employed to speed up the global convergence. In addition, to maintain efficiently the balance between exploration and exploitation, an adaptive nonlinear inertia weight is introduced into the SFLA algorithm. Further, a perturbation operator strategy based on Gaussian mutation is designed for local evolutionary, so as to help the best frog to jump out of any possible local optima and/or to refine its accuracy. In order to illustrate the efficiency of the proposed method (MS-SFLA), 23 well-known numerical function optimization problems and 25 benchmark functions of CEC2005 are selected as testing functions. The experimental results show that the enhanced SFLA has a faster convergence speed and better search ability than other relevant methods for almost all functions.

Shuffled Frog Leaping Algorithm for Hardware/Software Partitioning

Reconfigurable system on chip is well known for its flexibility for high performance embedded systems. The hardware/software (HW/SW) partitioning is the most important phase during the design of reconfigurable system on chip. A great many different algorithms have been adopted for solving the hardware/software partitioning problem. Shuffled Frog Leaping Algorithm (SFLA) is popular for its simple concepts, little parameter adjustment, high calculation speed, strong global search optimization capability and easy execution. In this paper, we apply the SFLA algorithm to solving hardware/software partitioning problem on reconfigurable system on chip with coarsegrained. The experimental results show that the SFLA algorithm can reduce the time cost by 45.54% on average with three different area constrain, compared with greedy algorithm. The time cost of SFLA algorithm are also reduced by 23.57% and 9.99% on average with simulated annealing algorithm (SA) and combined algorithm with greedy and simulated annealing algorithm (GSA). When area constrain is a half of area cost which all tasks are implemented by hardware will be taken, SFLA algorithm can reduce the time cost by 51.30%, 21.04% and 11.61% on average, compared with that of Greedy algorithm, SA, and GSA, respectively.

Fast MR brain image segmentation based on modified Shuffled Frog Leaping Algorithm

Due to the need of correct diseases analysis, MR image segmentation remains till now a challenging problem, especially in the presence of random noise. This paper proposes a new meta-heuristic algorithm for MR brain image segmentation, named Modified Shuffled Frog Leaping Algorithm (MSFLA), based on the technique of Shuffled Frog Leaping Algorithm (SFLA). In this new paradigm, there is no need to filter the original image. The new fitness function proposed in our algorithm helps to evaluate quickly the particle frogs in order to arrange them in descending order. The proposed approach has been compared with other meta-heuristics such as 3D-Otsu thresholding with SFLA and Genetic Algorithm (GA) and also with the algorithm of segmentation using the Rician Classifier (RiCE). Experimental results show that the proposed MSFLA is able to achieve better segmentation quality and execution time than the latest methods.