Improved Shuffled Frog Leaping Algorithm Research Articles

IoT service composition based on improved Shuffled Frog Leaping Algorithm

In the complex Internet of Things (IoT) environment, a plethora of IoT services with akin functions but varying qualities of service exist. To meet diverse customer needs and drive widespread application, service composition optimization becomes crucial. In the current era of rapid development in artificial intelligence, intelligent algorithms play a significant role in optimizing service composition. However, algorithms applied to IoT service composition optimization face common challenges of low search efficiency and insufficient optimization precision, including the Shuffled Frog Leaping Algorithm (SFLA) and Genetic Algorithm (GA). Therefore, this study seeks to enhance the perception of service quality in IoT service composition. It proposes an improved SFLA (ISFLA) based on the original SFLA. The algorithm integrates chaos theory and reverse learning theory for the acquisition of the initial population. It utilizes Euclidean distance to partition the population into groups and employs Gaussian mutation to optimize the optimal individual of each group. Finally, the entire population undergoes evolution through a local update method based on two strategies. Simulated experiments were conducted to search for optimal IoT service composition solutions of different scales. The results indicate that, compared to the SFLA, GA, ISFLA*, IGSFLA and SFLAGA, ISFLA achieves superior fitness values, better composition solutions, and exhibits faster convergence, higher stability, and greater overall operational efficiency.

A scheduling optimization method for stacker path in double-ended compact storage system

Given the low space usage rate of the traditional automated storage/retrieval system and the long aisle, it is easy for a stacker to take a long time to enter/leave the warehouse. Thus, a new type of double-ended compact storage system is proposed. This paper addresses the scheduling problem for the stacker to execute the single and dual commands mixed tasks in the system where the I/O ports are located at both ends of the aisle, and the power conveyor devices on the rack can meet the requirement of multi-depth storage and generate displacement. An improved shuffled frog leaping algorithm (ISFLA) is developed for the scheduling problem. In order to eliminate the disadvantages of local optimum and slow convergence in the standard shuffled frog leaping algorithm, a set of hybrid perturbation update methods are designed based on a role model learning strategy, and the feasibility of the improved algorithm is verified by a numerical simulation. The experimental results show that the solution quality and the convergence ability of the ISFLA are significantly improved, and it can effectively solve the stacker-scheduling problem in the double-ended compact storage system.

Modelling and simulation of photovoltaic system using hybrid improved shuffled frog leaping algorithm -Fuzzy controller under partial shaded condition

Due to its technical characteristics and economic benefits, the use of the photovoltaic (PV) system has become very common in the world. Nevertheless, the intermittent behavior of solar systems has created challenges for power grids due to their strong dependence on radiation and temperature. Moreover, PV systems must be operated at their maximum power so that they can return the investment costs in less time and also have higher efficiency. As a result, it is crucial to propose new maximum power point tracking (MPPT) methods to track the maximum power in conditions of uncertainty. To cope with this challenge, the fuzzy logic controller (FLC) is very effective approach. This study suggests the FCL for the MPPT algorithm for an isolated PV system using a push-pull converter. The suggested technique is optimized using the improved shuffled frog leaping algorithm (ISFLA) to improve the current MPPT controllers. To deal with the intermittent behavior of solar systems during peak load conditions, Grid is used to respond to the essential load. Fast convergence and low power fluctuations are the advantages of the recommended algorithm in tracing the MPP. The MATLAB/SIMULINK software is used to confirm the effectiveness of the recommended scheme. According to the simulation results, the PV system efficiency is increased to 99.7%.

An Optimized Neural Network Prediction Model for Reservoir Porosity Based on Improved Shuffled Frog Leaping Algorithm

Efficient and accurate porosity prediction is essential for the fine description of reservoirs, for which an optimized BP neural network (BPNN) prediction model is proposed. Aiming at the problem that the BPNN is sensitive to initialization and converges to local optimum easily, an improved shuffled frog leaping algorithm (ISFLA) is proposed based on roulette and genetic coding. Firstly, a roulette mechanism is introduced to improve the selection probability of elite individuals, thus enhancing the global optimization ability. Secondly, a genetic coding method is carried out by making full use of effective information such as the global and local optimal solutions and the boundary values of subgroups. Subsequently, the ISFLA algorithm is verified on 12 benchmark functions and compared with four intelligent optimization algorithms, and experimental results show its good optimization performance. Finally, the ISFLA algorithm is applied to the optimization of initial weights and thresholds of the BPNN, and a new model named ISFLA_BP is proposed to study the porosity prediction problem. The logging data is preprocessed by grey correlation analysis and deviation normalization, and then the effective prediction of porosity is achieved by natural gamma, density and other relevant parameters. The performance of ISFLA_BP model is compared with the standard three-layer BPNN and four BPNN parameter optimization methods based on swarm intelligence algorithms. Experimental results show that the proposed model has higher training accuracy, stability and faster convergence speed, with a mean square error of 0.02, and its prediction accuracy for porosity is higher than that of the other five methods.

An enhanced fuzzy-based clustering protocol with an improved shuffled frog leaping algorithm for WSNs

Wireless sensor networks (WSNs), an important technology for the Internet of Things (IoT), consist of the communications among small sensor nodes in a large-scale network. Due to the limited battery capacity of each small node, a clustering-based approach is used to conserve energy among the nodes and increase the network lifetime. The tasks of a clustering-based protocol are related mainly to the selection of the most appropriate cluster head nodes (CHs) and next-hop nodes (NHs) based on many influencing factors that require an optimal decision criterion, which can be addressed using a fuzzy inference system (FIS). Although FISs can provide satisfying decisions for selecting CHs and NHs for clustering protocols, FIS components such as fuzzy input variables, fuzzy rules, and fuzzy membership functions continue to be defined manually in most methods. Thus, these parameters must be tuned appropriately for specific applications. Therefore, in this paper, an enhanced fuzzy-based clustering protocol and an improved shuffled frog leaping algorithm (ISFLA) are proposed. In the proposed protocol, named EFC-ISFLA, a fuzzy-based clustering protocol optimized by the ISFLA is developed to maintain the network lifetime. The appropriate CHs are selected based on the energy threshold and optimized FIS with respect to the distance between adjacent CHs defined by the overlay boundary, resulting in reduced energy consumption and a longer network lifetime. Additionally, cluster formation and NH selection are performed based on the optimized FISs. A new encoding scheme is also designed to tune the network parameters and the FIS components simultaneously through the ISFLA. In the ISFLA, opposition-based operators and a surrogate model are adopted to address the limitations of the traditional SFLA. The experimental results show that the proposed technique provides better results in terms of maximizing the network lifetime, network stability, and total number of data packets delivered to the base station (BS).

Inversion Analysis of Seepage Parameters Based on Improved Shuffled Frog Leaping Algorithm

Aiming at overcoming the defects such as slow searching speed and easily trapping into local extremum at anaphase of the shuffled frog leaping algorithm (SFLA), based on the Evolutionary Exploration strategy, a more effective shuffled frog leaping algorithm, Improved Shuffled Frog Leaping Algorithm (ISFLA), which can be applied to the inverse analysis of seepage parameters to dams, is proposed. With the introduction of the threshold value selection in the local search of the original initial population to improve the best frogs in memeplex, the improved algorithm overcomes the shortcomings of traditional SFLA which can easily fall into a local optimum. By comparative analysis between the laboratory test and numerical simulation, the effectiveness and accuracy of ISFLA are demonstrated by the application to the inversion analysis of seepage parameters of earth dams. Furthermore, the inversion analysis of seepage parameters to the earth dam in Lianyungang China is studied by the ISFLA. Moreover, the seepage characteristics of the dam are evaluated; thus, the suggestion that the dam should be reinforced is put forward. All the results show that ISFLA in an inverse analysis of seepage parameters of dams has excellent value to hydropower engineering.

Study on border patrol task planning of heterogeneous UAVs group based on swarm intelligence.

Heterogeneous UAVs performing patrol tasks are a new type of border patrol method with high flexibility, high patrol efficiency, and low operating cost In order to improve the ability of heterogeneous UAVs to perform border patrol task, by constructing a three-dimensional complex coordinated planning model, a multi-objective fitness function with the minimum patrol energy consumption and maximum patrol coverage of UAVs in a complex mountain environment is established. Design an improved shuffled frog leaping algorithm (ISFLA) based on spiral search mechanism to solve the problem of task planning in complex mountain environment. The proposed algorithm is verified by simulation experiments. The simulation results show that the ISFLA algorithm for solving the path problem in complex three-dimensional environment has significantly improved the solving efficiency, accuracy and global convergence compared with the particle swarm optimization (PSO), differential evolution (DE) and shuffled frog leaping algorithm (SFLA). The experiments show that the proposed algorithm also has excellent solving ability in solving complex planning problems.

A Hybrid Approach Combining Improved Shuffled Frog-Leaping Algorithm With Dynamic Programming for Disassembly Process Planning

This paper proposes a hybrid approach which combines Improved Shuffled Frog-Leaping Algorithm (ISFLA) with Dynamic Programming (DP) for disassembly process planning (DPP). The DPP is a new frontier to the end-of-life product management which can help protect our environment. In the DPP, two tasks are to be accomplished. One is the determination of a disassembly sequence for components and another is the assignment of each component with a responsible operator. In the hybrid approach, the Shuffled Frog-Leaping Algorithm is used to accomplish the first task while the dynamic programming is used to complete the second task. Preliminary experimental results show the proposed hybrid approach outperforms the standard Shuffled Frog-Leaping Algorithm and improved Shuffled Frog-Leaping Algorithm. The comparison to a MILP has further confirmed that the proposed hybrid approach can find the optimal/near-optimal solution in terms of disassembly time. Further experiments show that the hybrid approach outperforms a genetic GA and MA. The results show the potential of using hybrid approaches for disassembly process planning.

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).

Parameter estimation of extended Jiles–Atherton hysteresis model based on ISFLA

This study introduces an extended Jiles–Atherton (JA) hysteresis model, which considers dynamic loss and anisotropy. The two considerations facilitate an accurate non-linearity representation of electromagnetic devices, thereby resulting in a precise agreement between the simulated and measured hysteresis loops. To achieve this goal, JA hysteresis model parameters must be estimated by an optimisation algorithm. Therefore, an improved shuffled frog-leaping algorithm (ISFLA) is proposed in this study. Differential evolution (DE) mutation operator, adaptive step size factor, and inertia weight factor are considered. Then, the implementation of the ISFLA is discussed using MATLAB. The proposed ISFLA is verified by the measured hysteresis loops of grain-oriented silicon toroidal core. The comparison between genetic algorithm, simulated annealing, DE, and ISFLA is discussed. Results show that the proposed ISFLA demonstrates better global optimum ability, lower computational burden, and faster convergence rate than the other three methods.

Research on Software Defect Prediction Framework Based on ISFLA in IoT Communication Software

Software defect feature selection has problems of feature space dimensionality reduction and large search space. This research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm (ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages, the feature values are sorted, and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively slow. The framework improves generalization of predictions of unknown data samples and enhances the ability to search for features related to learning tasks. At the same time, this framework further reduces the dimension of the feature space. After the contrast simulation experiment with other common defect prediction methods, we used the actual test data set to verify the framework for multiple iterations on Internet of Things (IoT) system platform. The experimental results show that the software defect prediction feature selection framework based on ISFLA is very effective in defect prediction of IoT communication software. This framework can save the testing time of IoT communication software, effectively improve the performance of software defect prediction, and ensure the software quality.

An Improved Shuffled Frog-Leaping Algorithm for Solving the Dynamic and Continuous Berth Allocation Problem (DCBAP)

This research deals with the dynamic and continuous berth allocation problem (DCBAP) in which both arrived and incoming ships are considered and a quay is used as a continuous line to accommodate as many ships as possible at one time. The DCBAP is solved by a two-stage procedure. In the first stage a heuristic/metaheuristic is used to generate alternative ship placement sequences while in the second stage a specific heuristic is employed to place ships and resolve overlaps of ships for the development of a feasible solution. Different methods, including FCFS (First Come First Served), SFLA (Shuffled Frog-Leaping Algorithm), and ISFLA (Improved Shuffled Frog-Leaping Algorithm), were employed in the first stage for comparison. The experimental results show that the ISFLA outperforms the others in terms of solution quality, implying that the ISFLA has the potential to deal with the DCBAP in a container terminal.

Reconfiguration and DG Sizing and Placement Using Improved Shuffled Frog Leaping Algorithm

In this paper is proposed a reconfiguration methodology with the presence of Distributed Generation (DG), aimed at achieving the minimum power loss, minimum number of switching operation and minimum deviation of bus voltage while satisfying all constraints using improved shuffled frog leaping algorithm (ISFLA).The performance of the proposed method is examined on 33 and 69 bus IEEE test distribution systems. The ISFLA performance is evaluated with the well-known algorithm including of harmony search algorithm (HSA), refined genetic algorithm (GRA), particle swarm optimization (PSO), differential evolutionary (DE) and conventional SFLA. Simulation results showed that the total power loss and voltage bus minimum in primary distribution network can be reduced significantly. Also the results in different scenarios are showed that the simultaneous reconfiguration and DG placement method is better in less losses and also in more minimum voltage. Moreover, the ISFLA superiority is proved in comparison with the HAS, GRA, PSO, DE, and SFLA in view of more convergence speed and accuracy and also converges in less number iteration. Also, the performance of the proposed method is favorable compared to previous studies.

Using the Guided Fireworks Algorithm for Local Backlight Dimming

Local backlight dimming is a promising display technology, with good performance in improving the visual quality and reducing the power consumption of device displays. To set optimal backlight luminance, it is important to design high performance local dimming algorithms. In this paper, we focused on improving the quality of the displayed image, and take local backlight dimming as an optimization problem. In order to better evaluate the image quality, we used the structural similarity (SSIM) index as the image quality evaluation method, and built the model for the local dimming problem. To solve this optimization problem, we designed the local dimming algorithm based on the Fireworks Algorithm (FWA), which is a new evolutionary computation (EC) algorithm. To further improve the solution quality, we introduced a guiding strategy into the FWA and proposed an improved algorithm named the Guided Fireworks Algorithm (GFWA). Experimental results showed that the GFWA had a higher performance in local backlight dimming compared with the Look-Up Table (LUT) algorithm, the Improved Shuffled Frog Leaping Algorithm (ISFLA), and the FWA.

REDUCTION OF REAL POWER LOSS BY IMPROVED SHUFFLED FROG-LEAPING ALGORITHM

This paper presents Improved Shuffled Frog-Leaping (ISFL) algorithm for solving optimal reactive power problem. A new search-acceleration parameter has been introduced into the formulation of the original shuffled frog leaping (SFL) algorithm to create an adapted form of the shuffled frog algorithm for solving the reactive power problem. The shuffled frog-leaping algorithm draws its formulation from two other search techniques: the local search of the ‘particle swarm optimization’ technique; and the competitiveness mixing of information of the ‘shuffled complex evolution’ technique. Proposed Improved Shuffled Frog-Leaping (ISFL) algorithm has been tested in standard IEEE 30,57,118 & Practical 191 Utility (Indian) System bus test systems and simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss & control variables within the limits

REDUCTION OF REAL POWER LOSS BY IMPROVED SHUFFLED FROG-LEAPING ALGORITHM

REDUCTION OF REAL POWER LOSS BY IMPROVED SHUFFLED FROG-LEAPING ALGORITHM

Electrical resistivity imaging inversion: An ISFLA trained kernel principal component wavelet neural network approach

The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve above problems, a kernel principal component wavelet neural network (KPCWNN) trained by an improved shuffled frog leaping algorithm (ISFLA) method is proposed in this study. An additional kernel principal component (KPC) layer is applied to reduce the dimensionality of apparent resistivity data and increase the computational efficiency of wavelet neural network (WNN). Meanwhile, a novel ISFLA algorithm is adopted for improving the learning ability and inversion quality of WNN. In the proposed ISFLA, a hybrid LC mutation attractor is used to enhance the exploitation ability and a differential updating rule is used to enhance the exploration ability. Four groups of experiments are considered to demonstrate the feasibility of the proposed inversion method. The inversion results of the synthetic and field examples show that the introduced method is superior to other algorithms in terms of prediction accuracy and computational efficiency, which contribute to better inversion results.

A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization

To deal with the multi-objective optimization problems (MOPs), a meta-heuristic based on an improved shuffled frog leaping algorithm (ISFLA) which belongs to memetic evolution is presented. For the MOPs, both diversity maintenance and searching effectiveness are crucial for algorithm evolution. In this work, modified calculation of crowding distance to evaluate the density of a solution, memeplex clustering analyses based on a grid to divide the population, and new selection measure of global best individual are proposed to ensure the diversity of the algorithm. A multi-objective extremal optimization procedure (MEOP) is also introduced and incorporated into ISFLA to enable the algorithm to evolve more effectively. Finally, the experimental tests on thirteen unconstrained MOPs and DTLZ many-objective problems show that the proposed algorithm is flexible to handle MOPs and many-objective problems. The effectiveness and robustness of the proposed algorithm are also analyzed in detail.

An Improved Shuffled Frog Leaping Algorithm and Its Application in Dynamic Emergency Vehicle Dispatching

The traditional method for solving the dynamic emergency vehicle dispatching problem can only get a local optimal strategy in each horizon. In order to obtain the dispatching strategy that can better respond to changes in road conditions during the whole dispatching process, the real-time and time-dependent link travel speeds are fused, and a time-dependent polygonal-shaped link travel speed function is set up to simulate the predictable changes in road conditions. Response times, accident severity, and accident time windows are taken as key factors to build an emergency vehicle dispatching model integrating dynamic emergency vehicle routing and selection. For the unpredictable changes in road conditions caused by accidents, the dispatching strategy is adjusted based on the real-time link travel speed. In order to solve the dynamic emergency vehicle dispatching model, an improved shuffled frog leaping algorithm (ISFLA) is proposed. The global search of the improved algorithm uses the probability model of estimation of distribution algorithm to avoid the partial optimal solution. Based on the Beijing expressway network, the efficacy of the model and the improved algorithm were tested from three aspects. The results have shown the following: (1) Compared with SFLA, the optimization performance of ISFLA is getting better and better with the increase of the number of decision variables. When the possible emergency vehicle selection strategies are 815, the objective function value of optimal selection strategies obtained by the base algorithm is 210.10% larger than that of ISFLA. (2) The prediction error of the travel speed affects the accuracy of the initial emergency vehicle dispatching. The prediction error of ±10 can basically meet the requirements of the initial dispatching. (3) The adjustment of emergency vehicle dispatching strategy can successfully bypassed road sections affected by accidents and shorten the response time.

An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction

Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study.