Shuffled Frog Leaping Algorithm Research Articles

Techno-economic analysis for rustic electrification in Egypt using multi-source renewable energy based on PV/ wind/ FC

A technical-economic investigation based on mathematical modeling, simulation, and optimization approach is employed in this research to assemble an island combined renewable energy systems (CRES) consists of solar PV/Wind/Fuel Cell (FC) of a small-scale countryside area in Egypt. The intent of the proposed island CRES is to boost the share of renewable energy in the energy mix and to study the possibility of using fuel cells as a storage/backup system instead of using battery banks.Three combinations of CRES are presented in this research to select the most optimum one. The combinations of the hybrid systems are PV/FC, PV/WT/FC, and WT/FC. The performance and the total cost of the suggested CRES were optimized using Firefly Algorithm (FA). The results obtained from the FA are compared with those obtained from the Shuffled Frog Leaping Algorithm (SFLA) and the particle swarm optimization (PSO).The selected case study area with latitude and longitude of (29.0214 N, 30.8714 E) is identified for economic viability in this work.The simulation outcomes show that the solar PV/Wind/Fuel Cell combination incorporated with an electrolyzer for hydrogen production grants the excellent performance. The proposed system is economically viable with a levelized cost of energy of 0.47 $/kWh.

A review: On path planning strategies for navigation of mobile robot

Abstract This paper presents the rigorous study of mobile robot navigation techniques used so far. The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigation over static and dynamic condition is analyzed (for single and multiple robot systems) and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches. It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm. Hence, reactive approaches are more popular and widely used for path planning of mobile robot. The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.

A New Pulse Coupled Neural Network (PCNN) for Brain Medical Image Fusion Empowered by Shuffled Frog Leaping Algorithm.

Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer’s disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly.

An efficient design of finite impulse response — Fractional-order differentiator using shuffled frog leaping algorithm heuristic

A fractional-order digital differentiator is employed for the calculation of a time-derivative of the applied signal. In the recent few decades, this particular concept of a fractional derivative has been gaining a lot of attention in various applications concerning engineering, technology and science that includes image processing along with automatic control. Once there has been an effective use for this continuous-time Fractional-Order Differentiator (FOD), the trend in its research is primarily toward using a discrete-time fractional differentiator. All these conventional techniques tend to make use of a unimodal function for approximating an ideal FOD. For these techniques, there is a minimization of the fitness function that is accomplished by the algorithms which are based on the gradient. The fractional-order circuits along with their systems include an emerging area that has a high level of potential in aspects such as the biomedical instrumentation, control or signal processing. A digital differentiator is a tool that is extremely helpful in the determination and estimation of time derivatives of any given signal. Irrespective of the actual type of filter chosen (the Finite Impulse Response (FIR) or the Infinite-Length Impulse Response (IIR)), it is critical to bring down the complexity of computation needed for the implementation of the filter for a certain bandwidth and error of approximation. A metaheuristic algorithm normally has some advantages in the solving of problems which are Non-Deterministic Polynomial (NP)-hard. The Shuffled Frog Leaping Algorithm (SFLA) has been a new heuristic algorithm proposed in this work for the determination of optimal coefficients of the problem of FIR-FOD. A design for fractional-order-based digital differentiator is not a very important topic in research and signal processing.

Obtaining the Kinematics Solution of an Aerial Manipulator Using the Shuffled Frog-Leaping Algorithm

Рассмотрено кинематическое решение в реальном времени для манипулятора, прикрепленного к беспилотному летательному аппарату; движение самого транспортного средства в данном исследовании не анализируется. Представленное кинематическое решение для манипулятора основано на модели Денавита - Хартенберга. Основной целью исследования является получение глобального решения в реальном времени для конфигурации и проектирования с взвешенной целевой функцией с наложением некоторых ограничений. Применение уравнений прямой кинематики манипулятора, полученных в результате исследования, позволяет превратить задачу планирования траектории в задачу оптимизации. Хорошо известны несколько типов вычислительных методов для решения ограниченных сложных нелинейных функций. В данном исследовании предлагается модифицированный алгоритм прыжка лягушки (SFLA), который является одним из методов искусственного интеллекта и рассматривается как метод поиска. Это ограниченный метаэвристический и популяционный подход. С его помощью представляется возможным решение обратной кинематической задачи с учетом мобильности платформы. Кроме того, данный метод предотвращает появление сингулярных точек, поскольку он не требует инверсии матрицы Якоби. Результаты экспериментального моделирования для планирования траектории манипулятора с шестью степенями свободы подтвердили целесообразность и эффективность предлагаемого метода.

MCDMSR: multicriteria decision making selection/replacement based on agility strategy for real optimization problems

Selection/replacements are an indispensable part in evolutionary algorithms (EAs), which are generally based on a single evaluation criterion. However, selections in nature are based on multi evaluation criteria, such as multi-aspect survival criteria of wolves (like running and attacking abilities). To realize more real survival of the fittest in EAs, multicriteria decision making selection/replacement (MCDMSR) is proposed. In fact, there is little research about using multicriteria decision making to improve selection/replacement models in EAs. VIKOR (a multicriteria decision making method) in management science is used in MCDMSR, and multi evaluation criteria of the populations are synthetically analyzed as a radar chart form. By using VIKOR, MCDMSR is able to respond to the population-state change in real time. MCDMSR is characterized by this agility, and this agility is derived from the decision making ability of VIKOR. Moreover, principle analysis and discussions are given to explain the feasibility of multicriteria decision making in the applications of selection/replacements. We provided the applications of MCDMSR in simple genetic algorithm, particle swarm optimization, artificial fish swarm algorithm, and shuffled frog leaping algorithm, by comparing with tournament selection, fine-grained tournament selection, all-individual-guider replacement, CD/RW, constrained-visual-region replacement, group constrained-visual-region replacement, part-individual-guider replacement for 36 benchmarks (i.e., 5 unimodal and 15 multimodal problems in CEC 2013 test suite, and 16 P-Peak problems). The effectiveness, efficiency, and diversity results of MCDMSR were acceptable.

A multiobjective adaptive approach for the inference of evolutionary relationships in protein-based scenarios

Complex optimization problem solving is a constant issue in a wide range of scientific domains. Robust bioinspired procedures with accurate search capabilities are therefore required to address the challenge that such optimization problems represent. This work explores different design alternatives for the metaheuristic Multiobjective Shuffled Frog-Leaping Algorithm, a novel method that combines parallel searches and swarm-based operators to undertake the processing of complex search spaces. Three variants of the metaheuristic are adopted: a dominance-based approach, an indicator-based alternative, and an adaptive proposal that incorporates both multiobjective strategies (dynamically assigning during the execution more resources to the most successful strategy). The performance of the proposed designs is examined when tackling, as a case study, the inference of ancestral relationships from protein data, using different multiobjective metrics and bio-statistical testing procedures. Experimental results show the additional robustness that the adaptive technique provides to the metaheuristic, allowing its search engine to exploit the most fitting multiobjective approach according to the status of the optimization process.

The multi-objective operation for cascade reservoirs using MMOSFLA with emphasis on power generation and ecological benefit

Abstract To efficiently develop power generation and solve downstream ecological health protection in Qingjiang basin, multi-objective ecological operation for cascade reservoirs (MOEOCR) model is established in contrast to conventional models that set ecological water requirement as constraint. The basic, suitable and ideal ecological water requirements in Geheyan and Gaobazhou sections are calculated using a requirement level index. Instead of the traditional evolution mode based on population, we introduce a shuffled frog leaping algorithm (SFLA) which evolves independently in sub-populations. Moreover, the SFLA is converted into a modified multi-objective algorithm (MMOSFLA) with strategies including chaotic population initialization, renewed frog grouping method and local search method, and elite frog set evolution based on cloud model. The water level corridor is used to help effectively handle complex constraints. The IGD and GD indexes are used to evaluate quality of solutions acquired by each method. In terms of normal year, the mean IGD and GD of MMOSFLA are 1.2 × 10–1 and 2.75 × 10–2, respectively. The scheduling results verify efficient search ability and convergence performance in solution diversity and distribution in comparison with other methods. Therefore, MMOSFLA is verified to provide an effective way to fulfill hydropower and ecological benefits facing the MOEOCR problem.

Color image quantization using the shuffled-frog leaping algorithm

Swarm-based algorithms define a family of methods that consider a population of very simple individuals that cooperate to solve a difficult problem. The Shuffled frog-leaping algorithm is a method of this type that has been applied to solve different types of problems. This article describes the application of this algorithm to the color quantization problem. Although the selected method was developed to solve optimization problems, this work shows how it can be adapted to solve the problem proposed. The proposed method uses the mean squared error as the objective function of the optimization problem to be solved. To reduce the execution time of the algorithm, it is applied to a subset of pixels of the original image. As a result, a quantized palette is obtained that is used to define the quantized image. Computational results indicate that the proposed method can generate a quantized image with low computational cost. Moreover, the quality of the image generated is better than that of the images obtained by several well-known color quantization methods.

Environmental economic dispatch with heat optimization in the presence of renewable energy based on modified shuffle frog leaping algorithm

In this paper, a new version of shuffle frog leaping algorithm (SFLA) which called modified SFLA (MSFLA) is proposed. To drive the proposed method both local and global search mechanisms in the original SFLA are modified. The local search mechanism is modified by introducing the movement inertia equation of particle swarm optimization (PSO). Furthermore, the global search mechanism is modified using the crossover and mutation operators of genetic algorithm (GA). Also, the formulation of the combined heat, emission and economic dispatch (CHEED) problem considering the availability of wind and solar power is presented where the objective functions of the CHEED problem are converted into a single objective function using price penalty function. Then, the proposed MSFLA is employed to solve the problem of CHEED with wind and solar power. To show the effectiveness of the proposed MSFLA, different test systems with different scenarios are used. In addition, the scalability of the proposed MSFLA is tested using large-scale test systems. The results of the proposed MSFLA are compared with those obtained by other heuristic and non-heuristic published methods. The comparison with other methods proves the efficiency and the superiority of the proposed MSFLA over other methods in all scenarios.

Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability

This study provides a new comparative analysis of four hybrid artificial intelligence models for the spatially explicit prediction of wildfire probabilities. Each model consists of an adaptive neuro-fuzzy inference system (ANFIS) combined with a metaheuristic optimization algorithm, i.e., genetic algorithm (GA), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA), and imperialist competitive algorithm (ICA). A spatial database was constructed based on 159 fire events from the Hyrcanian ecoregion (Iran) for which a suite of predictor variables was derived. Each predictor variable was discretized into classes. The step-wise weight assessment ratio analysis (SWARA) procedure was used to assign weights to each class of each predictor variable. Weights indicate the strength of the spatial relationship between each class and fire occurrence and were used for training the hybrid models. The hybrid models were validated using several performance metrics and compared to the single ANFIS model. Although the single ANFIS model outperformed the hybrid models in the training phase, its accuracy decreased considerably in the validation phase. All hybrid models performed well for both training and validation datasets, but the ANFIS-ICA hybrid showed superior predictive performance of spatially explicit wildfire prediction and mapping for the dataset. The results clearly demonstrate the ability of the optimization algorithms to overcome the over-fitting problem of the single ANFIS model at the learning stage of the fire pattern. This study contributes to the suite of research that seeks to obtain reliable estimates of relative likelihoods of natural hazards.

Severity of defect: an optimised prediction

To assure the quality of software an important activity is performed namely software defect prediction (SDP). Historical databases are used to detect software defects using different machine learning techniques. Conversely, there are disadvantages like testing becomes expensive, poor quality and so the product is unreliable for use. This paper classifies the severity of defects by using a method based on optimised neural network (NN). In full search space, a solution is found by many meta-heuristic optimisations and global search ability has been used. Hence, high-quality solutions are finding within a reasonable period of time. SDP performance is improved by the combination of meta-heuristic optimisation methods. For class imbalance problem, meta-heuristic optimisation methods such as genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) are applied. The above method is based on SFLA and the experimental outputs show that it can do better than Leven berg Marquardt based NN system (LM-NN).

Severity of defect: an optimised prediction

To assure the quality of software an important activity is performed namely software defect prediction (SDP). Historical databases are used to detect software defects using different machine learning techniques. Conversely, there are disadvantages like testing becomes expensive, poor quality and so the product is unreliable for use. This paper classifies the severity of defects by using a method based on optimised neural network (NN). In full search space, a solution is found by many meta-heuristic optimisations and global search ability has been used. Hence, high-quality solutions are finding within a reasonable period of time. SDP performance is improved by the combination of meta-heuristic optimisation methods. For class imbalance problem, meta-heuristic optimisation methods such as genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) are applied. The above method is based on SFLA and the experimental outputs show that it can do better than Leven berg Marquardt based NN system (LM-NN).

Developing Shuffled Frog‐Leaping Algorithm (SFLA) Method to Solve Power Load‐Constrained TCRTO Problems in Civil Engineering

It is extensively acknowledged that excessive on‐site electricity power load often causes power failure across a construction site and surrounding residential zones and can result in unforeseen schedule delay, construction quality problems, life inconvenience, and even property loss. However, energy management, such as power load optimization, has long been ignored in construction scheduling. This study aims to develop a modified shuffled frog‐leaping algorithm (SFLA) approach in project scheduling to aid decision‐makers in identifying the best Pareto solution for time‐cost‐resource trade‐off (TCRTO) problems under the constraint of precedence, resource availability, and on‐site peak electricity power load. A mathematical model including three objective functions and five constraints was established followed by the application of the modified SLFA on real‐case multiobjective optimization problems in construction scheduling. The performance of SLFA was compared with that of the nondominated sorting genetic algorithm (NSGA II). The results showed that the developed new approach was superior in identifying optimal project planning solutions, which could essentially assist on‐site power load‐oriented schedule decision‐making for construction teams.

A New Application of Optimized Random Forest Algorithms in Intelligent Fault Location of Rudders

During the movements of aircraft, missiles, and ships, the rudder plays an important role in their direction control. In order to test the parameters of the rudders, we have to manually measure each item one by one in traditional production and manufacturing of rudders, which waste a great quantity of manpower and time. In this paper, we present a new application in rudder fault test by using machine learning technology and recommend a new intelligent method for fault location. The main subject revolves around prediction-oriented problems of multi-dimensional performance parameters data mining and the modeling of classification, including the analysis and processing of data features and the solution of fault location based on classification model. In addition, to improve the accuracy of the classification model, we optimized the random forest (RF) algorithm with the shuffled frog leaping algorithm (SFLA), which we call shuffled frog leaping algorithm-based random forest (SFLA-RF). It effectively solves the problem of voting competition among each tree, which makes the decision of the model more efficient and accurate. In a word, by means of automatic test and intelligent analysis, this new method breaks through the technical bottleneck of low efficiency of parameters test and the shortcomings of traditional rudder fault location.

Genetic–Frog-leaping Algorithm for Text Document Clustering

In recent years, the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web. As a result, the use of techniques for extracting useful information from large collections of data, and particularly documents, has become more necessary and challenging. Text clustering is such a technique; it consists in dividing a set of text documents into clusters (groups), so that documents within the same cluster are closely related, whereas documents in different clusters are as different as possible. Clustering depends on measuring the content (i.e., words) of a document in terms of relevance. Nevertheless, as documents usually contain a large number of words, some of them may be irrelevant to the topic under consideration or redundant. This can confuse and complicate the clustering process and make it less accurate. Accordingly, feature selection methods have been employed to reduce data dimensionality by selecting the most relevant features. In this study, we developed a text document clustering optimization model using a novel genetic frog-leaping algorithm that efficiently clusters text documents based on selected features. The proposed approach is based on two metaheuristic algorithms: a genetic algorithm (GA) and a shuffled frog-leaping algorithm (SFLA). The GA performs feature selection, and the SFLA performs clustering. To evaluate its effectiveness, the proposed approach was tested on a well-known text document dataset: the “20Newsgroup” dataset from the University of California Irvine Machine Learning Repository. Overall, after multiple experiments were compared and analyzed, it was demonstrated that using the proposed algorithm on the 20Newsgroup dataset greatly facilitated text document clustering, compared with classical K-means clustering. Nevertheless, this improvement requires longer computational time.

Lévy Flight Shuffle Frog Leaping Algorithm Based on Differential Perturbation and Quasi-Newton Search

Levy flight Shuffle Frog Leaping Algorithm (LSFLA) is a SFLA variant and enhances the performance of SFLA largely, however, it still has some defects, such as poor convergence and low efficiency. So an improved LSFLA, namely, LSFLA based on Differential perturbation and Quasi-Newton search (DQLSFLA), is proposed in this paper. Firstly, the way of updating only one solution which is the worst one at every sub-iteration in LSFLA is replaced with an all-solution updating way in the subgroup to improve the probability of obtaining the best solution, to omit one sorting step at every sub-iteration and the sub-iteration number parameter setting, to reduce the computational complexity and to enhance the optimization efficiency. Secondly, a random differential perturbation approach and an improved Levy flight updating one are created and used in the subgroup updating to form DLSFLA, that is, the first half frogs in the subgroup use the random differential perturbation updating approach to improve the global search ability, and the last half adopt the improved Levy flight updating method to keep the advantage of the Levy flight updating method and overcome its defects. Finally, quasi-Newton local search is integrated into DLSFLA near the end of the algorithm to improve the convergence speed. Experimental results on the complex functions from CEC-2014 show that DQLSFLA's convergence quality and optimization efficiency are much better than LSFLA's and that compared with quite a few other state-of-the-art algorithms, DQLSFLA has better performance. The results on medical image enhancement and Quadratic Assignment Problem (QAP) also show that DQLSFLA can solve the real word optimization problems better than LSFLA can.

Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design

In this study, we aim to develop a system optimization model of Railway Freight Transportation Routing Design (RFTRD) and conduct solution analysis which is based on the improved multi-objective swarm intelligence algorithm. The proposed improved multi-objective swarm intelligence algorithm is applied to solve the combinatorial optimization problem of railway door-to-door freight transportation through design, and provide decision support for railway vehicle door-to-door freight transportation through design. The optimization results shows that, the random multi-neighborhood based multi-objective shuffled frog-leaping algorithm with path relinking (RMN-MOSFLA-PR) can be better applied to solve the combined multi-objective optimization problem, and this proposed improved algorithm can find Pareto frontier through the comparative analysis in the design example of railway door-to-door freight transportation. The frontier can provide support for railway transportation enterprises, arrange the decision-making of the starting and ending stations for multiple shippers, and optimize the use of existing transportation resources, so as to reduce the transportation cost and time of the system.

An improved SFLA for flexible job shop scheduling problem considering energy consumption

In this paper, a multi-objective flexible job shop scheduling problem (MOFJSP) was studied systematically. A novel energy-saving scheduling model was established based on considering makespan and total energy consumption simultaneously. Different from previous studies, four types of energy consumption were considered in this model, including processing energy, idle energy, transport energy, and turn-on/off energy. In addition, a turn-off strategy is adopted for energy-saving. A modified shuffled frog-leaping algorithm (SFLA) was applied to solve this model. Moreover, operators of multi-point crossover and neighborhood search were both employed to obtain optimal solutions. Experiments were conducted to verify the performance of the SFLA compared with a non-dominated sorting genetic algorithm with blood variation (BVNSGA-II). The results show that this algorithm and strategy are very effective.

Optimal Scheduling and Economic Analysis of Hybrid Electric Vehicles in a Microgrid

Abstract A projected high penetration of electric vehicles (EVs) in the electricity market will introduce an additional load in the grid. The foremost concern of EV owners is to reduce charging expenditure during real-time pricing. This paper presents an optimal charging schedule of the electric vehicle with the objective to minimize the charging cost and charging time. The allocation of EVs should satisfy constraints related to charging stations (CSs) status. The results obtained are compared with the two conventional algorithms and other charging algorithms: Arrival time-based priority algorithm (ATP) and SOC based priority algorithm (SPB), Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA). Also, the CS is powered by the main grid and the microgrid available in the CSs. The EVs charging schedule and the economic analysis is done for two cases: (i) With Grid only (ii) With Combined Grid & microgrid. The load shifting of EVs is done based on the grid pricing and the results obtained are compared with the other cases mentioned.