Frog Leaping Algorithm Research Articles

The optimization and operation of multi-energy-coupled microgrids by the improved fireworks algorithm-shuffled frog-leaping algorithm.

This study aims to address optimization and operational challenges in multi-energy coupled microgrids to enhance system stability and reliability. After analyzing the requirements of such systems within comprehensive energy systems, an improved fireworks algorithm (IFWA) is proposed. This algorithm combines an adaptive resource allocation strategy with a community genetic strategy, automatically adjusting explosion range and spark quantity based on individual optimization status to meet actual needs. Additionally, a multi-objective optimization model considering active power network losses and static voltage is constructed, utilizing the shuffled frog-leaping algorithm (SFLA) to solve constrained multi-objective optimization problems. Through simulation experiments on a typical northern comprehensive energy system, conducted with a scheduling period of T = 24, the feasibility and superiority of IFWA-SFLA are validated. Results indicate that IFWA-SFLA performs well in optimizing microgrid stability, managing electrical energy flow effectively within the microgrid, and reducing voltage fluctuations. Furthermore, the circuit structure and control strategy of microgrid energy storage bidirectional inverters based on IFWA are discussed, along with relevant simulation results.

An Improved Shuffled Frog-Leaping Algorithm to Solving 0–1 Knapsack Problem

An Improved Shuffled Frog-Leaping Algorithm to Solving 0–1 Knapsack Problem

Forensic Characterization of Mobile Phone Cases Using Infrared Spectroscopy with Deep Learning Methods

The fast and nondestructive identification of mobile phone cases is a crucial task for law enforcement personnel in the field of forensic science. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) has been employed in conjunction with feature extraction and deep learning algorithms to develop a method for distinguishing mobile phone cases. As preprocessing steps, multivariate scatter correction and standard normal variate were performed. Feature variables were extracted using the competitive adaptive reweighted sampling and shuffled frog leaping algorithm. Several supervised pattern recognition methods, namely ResNet50, VGG16, and multilayer perceptron (MLP), were utilized for constructing classifiers. The results indicated that the model’s performance based on multivariate scattering correction (MSC) with competitive adaptive re-weighted sampling (CARS) surpassed that of other approaches. Notably, the ResNet50 model exhibited superior discriminative ability compared to the VGG16 and MLP models, achieving 100% accuracy in the differentiation of all samples. This designed method represents a potentially rapid and nondestructive approach for identifying mobile phone cases in forensic science.

Trust And Energy-Aware Routing Protocol for Wireless Sensor Networks Based on Secure Routing

Wireless Sensor Network (WSN) is a network area that includes a large number of nodes and the ability of wireless transmission. WSNs are frequently employed for vital applications in which security and dependability are of utmost concern. The main objective of the proposed method is to design a WSN to maximize network longevity while minimizing power usage. In a WSN, trust management is employed to encourage node collaboration, which is crucial for achieving dependable transmission. In this research, a novel Trust and Energy Aware Routing Protocol (TEARP) in wireless sensors networks is proposed, which use blockchain technology to maintain the identity of the Sensor Nodes (SNs) and Aggregator Nodes (ANs). The proposed TEARP technique provides a thorough trust value for nodes based on their direct trust values and the filtering mechanisms generate the indirect trust values. Further, an enhanced threshold technique is employed to identify the most appropriate clustering heads based on dynamic changes in the extensive trust values and residual energy of the networks. Lastly, cluster heads should be routed in a secure manner using a Sand Cat Swarm Optimization Algorithm (SCSOA). The proposed method has been evaluated using specific parameters such as Network Lifetime, Residual Energy, Throughpu,t Packet Delivery Ratio, and Detection Accuracy respectively. The proposed TEARP method improves the network lifetime by 39.64%, 33.05%, and 27.16%, compared with Energy-efficient and Secure Routing (ESR), Multi-Objective nature-inspired algorithm based on Shuffled frog-leaping algorithm and Firefly Algorithm (MOSFA) , and Optimal Support Vector Machine (OSVM).

CO2 Emission-Constrained Short-Term Unit Commitment Problem Using Shuffled Frog Leaping Algorithm

The increasing concerns about greenhouse gas emissions have made it necessary to incorporate environmental constraints in the operation of power systems. The CO2 emission-constrained short-term unit commitment problem (CSCUCP) is a multiobjective optimization problem that involves minimizing both the cost of operation and the CO2 emissions. This paper proposes an integer-coded shuffled frog-leaping algorithm (SFLA) to minimize both total CO2 emissions and operating costs for the unit commitment problem (UCP) over a one-day scheduling period. The SFLA is inspired by the natural food-searching behavior of frogs. The proposed method aims to determine the optimal start-up and shut-down times for generating units to meet fluctuating loads while minimizing operating costs and CO2 emissions. The method takes into account fuel costs, start-up and shut-down costs, and maintenance costs while satisfying various constraints. The study uses the IEEE 39 bus with a 10-unit test system, and the results are related to conventional methods. The proposed method consistently produces lower CO2 emissions and total operating costs compared to the existing methods.

Optimal Power Dispatch of Multiple DGs Using a Hybrid Algorithm for Mitigating Voltage Deviations and Losses in a Radial Distribution System with Economic Benefits

In this research, a meta-heuristic-based hybrid algorithm was used to optimize the power dispatch of numerous Distributed Generators (DGs) in a Radial Distribution System (RDS) for hourly fluctuating seasonal loads in order to reduce losses and voltage variations while also saving money. With hourly seasonal load changes, renewable DGs like PV, Wind, and Hybrid (PV+Wind) were used. The HA is proposed in this paper as a way to achieve successful outcomes by merging two meta-heuristic algorithms. The findings of the HA are compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Shuffled Frog Leap Algorithm (SFLA), and Jaya Algorithm (JA) when they are applied to a standard IEEE 33 bus RDS and PG&E 69 bus RDS. According to the test findings simulated in the MATLAB environment, Hybrid Algorithm (HA) beat GA, PSO, SFLA, and JA in terms of optimal power dispatch of numerous DGs to minimise losses and voltage variations, as well as the cost-benefit analysis of renewable DGs energy generation.

Sustainable Green Energy Management: Optimizing Scheduling of Multi-Energy Systems Considered Energy Cost and Emission Using Attractive Repulsive Shuffled Frog-Leaping

As energy systems become increasingly complex, there is a growing need for sustainable and efficient energy management strategies that reduce greenhouse gas emissions. In this paper, multi-energy systems (MES) have emerged as a promising solution that integrates various energy sources and enables energy sharing between different sectors. The proposed model is based on using an Attractive Repulsive Shuffled Frog-Leaping (ARSFL) algorithm that optimizes the scheduling of energy resources, taking into account constraints such as capacity limitations and environmental regulations. The model considers different energy sources, including renewable energy and a power-to-gas (P2G) network with power grid, and incorporates a demand–response mechanism that allows consumers to adjust their energy consumption patterns in response to price signals and other incentives. The ARSFL algorithm demonstrates superior performance in managing and minimizing energy purchase uncertainty compared to the particle swarm optimization (PSO) and genetic algorithm (GA). It also exhibits significantly reduced execution time, saving approximately 1.59% compared to PSO and 2.7% compared to GA.

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

The most important research area in robotics is navigation algorithms. Robot path planning (RPP) is the process of choosing the best route for a mobile robot to take before it moves. Finding an ideal or nearly ideal path is referred to as “path planning optimization.” Finding the best solution values that satisfy a single or a number of objectives, such as the shortest, smoothest, and safest path, is the goal. The objective of this study is to present an overview of navigation strategies for mobile robots that utilize three classical approaches, namely: the roadmap approach (RM), cell decomposition (CD), and artificial potential fields (APF), in addition to eleven heuristic approaches, including the genetic algorithm (GA), ant colony optimization (ACO), artificial bee colony (ABC), gray wolf optimization (GWO), shuffled frog-leaping algorithm (SFLA), whale optimization algorithm (WOA), bacterial foraging optimization (BFO), firefly (FF) algorithm, cuckoo search (CS), and bat algorithm (BA), which may be used in various environmental situations. Multiple issues, including dynamic goals, static and dynamic environments, multiple robots, real-time simulation, kinematic analysis, and hybrid algorithms, are addressed in a different set of articles presented in this study. A discussion, as well as thorough tables and charts, will be presented at the end of this work to help readers understand what types of strategies for path planning are developed for use in a wide range of ecological contexts. Therefore, this work’s main contribution is that it provides a broad view of robot path planning, which will make it easier for scientists to study the topic in the near future.

TCSC Application for Transient Stabilization of Wind Turbine with Fixed Speed Induction Generator

Wind turbines (WTs) are a desirable alternative to traditional nonrenewable power resources as a result of recent environmental concerns. Some of them are fixed speed wind generator (FSWG) and have been integrated to power system by squirrel cage induction generator (SCIG). Induction machine absorbs reactive power during all operating conditions, especially at fault condition may result in severe voltage drop which can lead to generator outage. This outage disconnects a significant amount of active power and consequently leads to frequency instability. In order to prevent induction generator (IG) outages in short circuit failures, this paper investigates thyristor-controlled series capacitor (TCSC) device as a candidate solution. TCSC compensates the IG terminal voltage drop by adjusting transmission line impedance at fault condition. In the proposed method, a metaheuristic technique means that shuffled frog leaping algorithm (SFLA) has been utilized to optimize the TCSC controller gains. The proposed scheme can be applied for both SCIGs and wound rotor induction generators (WRIGs) which is another advantage of this method. Single-machine infinite bus system is considered as case study, and various operating conditions and disturbances have been considered to verify the effectiveness of the proposed method.

Guided shuffled frog-leaping algorithm for flexible job shop scheduling problem with variable sublots and overlapping in operations

In this study, a guided shuffled frog-leaping algorithm (GSFLA) is proposed to minimise the makespan of the flexible job shop scheduling problem with variable sublots and overlapping in operations (FJSP-VSOO). In order to express the solution space more efficiently and completely for the co-optimization of batching and scheduling problems, an operation-level batching encoding considering sub-batches splitting is designed. Considering the characteristics of overlapping in operations, an active decoding method based on the unit assignment and right-shift operation is developed. To ensure the quality and diversity of the initial population, a hybrid initialization method based on dynamic load balancing is proposed. In addition, to balance the global and local search capabilities of the GSFLA, a sublot disturbance based local search algorithm is embedded in the process of the priority guide memeplex evolution, which can adaptively adjust the search tendency of the algorithm through the priority guide operation. Finally, the effectiveness of the proposed methods is verified in extensive experiments based on instances of FJSP with lot streaming (LS) on different scales. Moreover, compared with some advanced algorithms, the results show that the GSFLA has superiority in terms of optimization ability and solution efficiency.

Parameter Estimation of Induction Motors using Hybrid GWO-CS Algorithm

This study investigates a hybrid algorithm between Grey Wolf Optimization (GWO) and Cuckoo Search (CS) algorithms to find the parameters of induction motors. The parameters of the induction motor have been estimated by using the data supplied by the manufacturer. The problem for parameter estimation of the induction motor is formulated as an optimization problem. Then, the optimization problem is solved by using GWO and hybrid algorithm based on GWO and CS algorithms for the estimation of induction motor parameters. Numerical results show that both algorithms are capable of solving the optimization problem for finding the parameters of induction motor. Also, two algorithms and other algorithms such as Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Shuffled Frog-Leaping Algorithm (SFLA), and Modified Shuffled Frog-Leaping Algorithm (MSFLA) are compared for the problem. The results show that the hybrid GWO-CS algorithm gives a smaller objective value and closer torque value to the manufacturer’s data than the GWO algorithm and several algorithms for motor 1. Hybrid GWO-CS algorithm gives nearly the same results with GWO algorithm for motor 2.

Origin-Oriented Shuffled Frog Leaping Vehicle Routing Multiobjective Optimization Algorithm

Shuffled frog leaping algorithm is a biological swarm intelligent optimization algorithm and improved into capacity-limited vehicle routing problem. However, the optimization performance is limited with improvement strategies in major of the improvement algorithm. A novel framework of algorithm is proposed to solve capacity-limited vehicle routing problem, including three modules such as origin oriented shuffled frog leaping algorithm strategy, origin oriented shuffled frog leaping vehicle routing multiobjective optimization algorithm strategy, and output module. The frog individuals gather near the origin with the maximum probability and in the area circle, with the frog leaping radius or frog-oriented radius, as the neighborhood. The negative value of the maximum entropy and the shortest total path length of the vehicle are selected as the fitness. The performance test shows that it overcomes the defect of slow convergence compared with other five algorithms. It performs well to solve vehicle routing problems.

Bio-communal wastewater treatment plant real-time modeling using an intelligent meta-heuristic approach: A sustainable and green ecosystem

Considering the importance of nitrogen and organic carbon in supporting the growth of various algae and organic matters, that improves eutrophication along the water bodies. It is, therefore, essential to develop reliable tools that can help policymakers and experts in-understand the environmental and aquatic importance of these macronutrients in relation to other different hydro-physical and chemical variables. The current research proposes the development of three different data intelligence; Neuro-fuzzy (NF), Support Vector Regression (SVR), and Multivariate Regression (MVR) model integrated with a hybrid Neuro-fuzzy-Shuffled frog-leaping algorithm (NF-SFLA) for the simulation of total Kjeldahl Nitrogen (TKNeff) and total organic carbon (TCODeff) based-organic removal from wastewater treatment plant (WWTP). Both visualized and numerical performance of the single models indicated that NF model combination two (NF-M2) showed superior performance than other models for modeling TKNeff (MAE = 0.0367, NSE = 0.9110) and TCODeff (MAE = 0.0363, NSE = 0.8300). The hybrid NF-SFLA was proposed to improve the performance prediction of both TKNeff and TCODeff from the WWTP effluent. The NF-SFLA-M2 achieves an overwhelming performance improvement of up to 13 % over NF-M2 for TKNeff and TCODeff regarding NSE criteria.

A cotton leaf nitrogen monitoring model based on spectral-fluorescence data fusion

In the present study, hyperspectral imaging and remote sensing of fluorescence were integrated to monitor the nitrogen content in leaves of drip-irrigated cotton at different growth periods in northern Xinjiang, China. Based on the spectrum and chlorophyll fluorescence parameters of nitrogen content in cotton leaves of different growth periods obtained through the shuffled frog-leaping algorithm (SFLA), the successive projection algorithm (SPA), grey relational analysis (GRA), and competitive adaptive reweighted sampling (CARS), a monitoring model of nitrogen content in cotton leaves was established via on hyperspectral imaging, chlorophyll fluorescence parameters, and spectral-fluorescence data fusion. The results showed that: (1) there were significant positive correlations between the chlorophyll fluorescence parameters Fv'/Fm', Fv/Fm, Yield, Fm, NPQ, and the nitrogen content at each growth period. (2) The effectiveness of chlorophyll fluorescence parameters in inversion of nitrogen content was the highest at the budding period and the blooming period, and the coefficients of determination (R2) of the validation sets were 0.745 and 0.709, respectively. (3) In the monitoring model for cotton leaf nitrogen in the blooming period that was established based on the decision-level algorithm and spectral-fluorescence data fusion, the R2 value of the training set reached 0.961, and that of the validation set was 0.828. In conclusion, the findings of this study suggest that the feature-level fusion and decision-level fusion algorithms of spectral-fluorescence data can effectively improve the accuracy and reliability of cotton leaf nitrogen monitoring.

A Shuffled Frog-Leaping Algorithm with Cooperations for Distributed Assembly Hybrid-Flow Shop Scheduling with Factory Eligibility

The distributed assembly scheduling problem with a hybrid-flow shop for fabrication is seldom studied, and some real-life constraints such as factory eligibility are seldom handled. In this study, a distributed assembly hybrid-flow shop-scheduling problem (DAHFSP) with factory eligibility is investigated, which has some symmetries on machines. A shuffled frog-leaping algorithm with cooperations (CSFLA) is applied to minimize makespan. A problem-related feature is used. Memeplexes are evaluated, and group 1, with the two best memeplexes, and group 2, with the two worst memeplexes, are formed. A new cooperation between memeplexes and an adaptive search strategy are implemented in groups 1 and 2, respectively. An adaptive cooperation between groups 1 and 2 is also given. Population shuffling is executed every T generations. A number of computational experiments are conducted. Computational results demonstrate that new strategies are effective and CSFLA is a very competitive algorithm for DAHFSP with factory eligibility.

Robot path planning based on shuffled frog leaping algorithm combined with genetic algorithm

When the shuffled frog leaping algorithm (SFLA) is used to solve the robot path planning problem in obstacle environment, the quality of the initial solution is not high, and the algorithm is easy to fall into local optimization. Herein, an improved SFLA named ISFLA combined with genetic algorithm is proposed. By introducing selection, crossover and mutation operators in genetic algorithm, the ISFLA not only improves the solution quality of the SFLA, but also accelerates its convergence speed. Moreover, the ISFLA also proposes a location update strategy based on the central frog, which makes full use of the global information to avoid the algorithm falling into local optimization. By comparing ISFLA with other algorithms including SFLA in the map environment of different obstacles, it is confirmed that ISFLA can effectively improve the minimum path optimization and robustness in the simulation experiments of mobile robots.

A shuffled frog-leaping algorithm with Q-learning for unrelated parallel machine scheduling with additional resource and learning effect

Unrelated parallel machine scheduling problem (UPMSP) with additional resources and UPMSP with learning effect have attracted some attention; however, UPMSP with additional resources and learning effect is seldom studied and meta-heuristics for UPMSP hardly possess reinforcement learning as new optimization mechanism. In this study, a shuffled frog-leaping algorithm with Q-learning (QSFLA) is presented to solve UPMSP with one additional resource and learning effect. A new solution presentation is presented. Two populations are obtained by division. A Q-learning algorithm is constructed to dynamically decide search operator and search times. It has 12 states depicted by population quality evaluation, four actions defined as search operators, a new reward function and a new action selection. Extensive experiments are conducted. Computational results demonstrate that QSFLA has promising advantages for the considered UPMSP.

Cloud-Based Advanced Shuffled Frog Leaping Algorithm for Tasks Scheduling.

In recent years, the world has seen incremental growth in online activities owing to which the volume of data in cloud servers has also been increasing exponentially. With rapidly increasing data, load on cloud servers has increased in the cloud computing environment. With rapidly evolving technology, various cloud-based systems were developed to enhance the user experience. But, the increased online activities around the globe have also increased data load on the cloud-based systems. To maintain the efficiency and performance of the applications hosted in cloud servers, task scheduling has become very important. The task scheduling process helps in reducing the makespan time and average cost by scheduling the tasks to virtual machines (VMs). The task scheduling depends on assigning tasks to VMs to process the incoming tasks. The task scheduling should follow some algorithm for assigning tasks to VMs. Many researchers have proposed different scheduling algorithms for task scheduling in the cloud computing environment. In this article, an advanced form of the shuffled frog optimization algorithm, which works on the nature and behavior of frogs searching for food, has been proposed. The authors have introduced a new algorithm to shuffle the position of frogs in memeplex to obtain the best result. By using this optimization technique, the cost function of the central processing unit, makespan, and fitness function were calculated. The fitness function is the sum of the budget cost function and the makespan time. The proposed method helps in reducing the makespan time as well as the average cost by scheduling the tasks to VMs effectively. Finally, the performance of the proposed advanced shuffled frog optimization method is compared with existing task scheduling methods such as whale optimization-based scheduler (W-Scheduler), sliced particle swarm optimization (SPSO-SA), inverted ant colony optimization algorithm, and static learning particle swarm optimization (SLPSO-SA) in terms of average cost and metric makespan. Experimentally, it was concluded that the proposed advanced frog optimization algorithm can schedule tasks to the VMs more effectively as compared with other scheduling methods with a makespan of 6, average cost of 4, and fitness of 10.

Optimal reliability allocation of heterogeneous components in pharmaceutical production plant

The COVID-19 pandemic and competitiveness pressure the pharmaceutical companies to acquire systems designed to be as reliable as possible. The present paper aims to optimize the design of a pharmaceutical plant through the reliability allocation of heterogeneous components under the design constraints. The problem is solved by resorting to three nature-inspired algorithms of artificial intelligence (AI): grey wolf optimizer (GWO), shuffled frog-leaping algorithm (SFLA), and adaptive particle swarm optimization (ADAP-PSO). A penalty function is implemented to handle the constraints and the results obtained are compared.

A hybrid rough set shuffled frog leaping knowledge inference system for diagnosis of lung cancer disease

Abundant medical data are generated in the digital world every second. However, gathering helpful information from these data is difficult. Gathering useful information from the dataset is very advantageous and demanding. Besides, such data also contain many extraneous features that do not influence the foreboding accuracy while diagnosing a disease. The data must eliminate these extraneous features to get a better diagnosis. Ultimately, the minimized information system will lead to a better diagnosis. In this paper, we have introduced an incremental rough set shuffled frog leaping algorithm for knowledge inference. The proposed algorithm helps find minimum features from an information system while handling complex databases with uncertainty and incompleteness. The proposed rough set shuffled frog leaping knowledge inference model works in two phases. In the initial phase, the incremental rough set shuffled frog leaping algorithm is used to get the most relevant features. Identifying the relevant features is carried out using a fitness function, which uses the rough degree of dependency. The use of the fitness function identifies the much information with the minimum number of features. The purpose of feature selection is to identify a feature subset from an original set of features without reducing the predictive accuracy and to scale back the computation overhead in the data processing. In the second phase, a rough set is utilized for knowledge discovery in perception with rule generation. The selection of decision rules is carried out based on the accuracy of the decision rule and a predefined threshold value. An empirical analysis of the lung disease information system and a comparative study is conducted. Experimental outcomes exhibit that hybrid techniques express the feasibility of the proposed model while achieving better classification accuracy.