Image Watermarking Scheme Based on Shuffled Frog Leaping Algorithm

Multistage distribution system expansion planning considering distributed generation using hybrid evolutionary algorithms

In this paper, the Authors present the designing of power system stabilizer (PSS) and static var compensator (SVC) based on chaos, particle swarm optimization (PSO) and shuffled frog leaping (SFL) Algorithms has been presented to improve the power system stability. Single machine infinite bus (SMIB) system with SVC located at the terminal of generator has been considered to evaluate the proposed SVC and PSS controllers. The coefficients of PSS and SVC controller have been optimized by Chaos, PSO and SFL algorithms. Fi-nally the system with proposed controllers is simulated for the special disturbance in input power of genera-tor, and then the dynamic responses of generator have been presented. The simulation results show that the system composed with recommended controller has outstanding operation in fast damping of oscillations of power system and describes an application of Chaos, PSO and SFL algorithms to the problem of designing a Lead-Lag controller used in PSS and SVC in power system.

The shuffled frog leaping algorithm (SFLA) is presented, to optimally design multiple proportional–integral (PI) controllers of the static synchronous compensator (STATCOM) system with the purpose of improving the transient stability of a grid‐connected wind farm. The control strategy of the STATCOM system depends on a sinusoidal pulse width modulation voltage source converter, which is controlled by the cascaded PI control scheme. The response surface methodology is used to establish a second‐order fitted model of the transient voltage responses at the point of common coupling in terms of PI controllers’ parameters. For realistic responses, a three‐mass drive train model is used for the wind turbine generator system because of its great influence on the dynamic analyses. The SFLA code is built using MATLAB software. The effectiveness of optimised PI‐controlled STATCOM by the SFLA is then compared to that optimised by genetic algorithms technique taking into consideration symmetrical and unsymmetrical fault conditions. The proposed control scheme is applied to a real grid‐connected wind farm system at Hokkaido Island, Japan. The validity of the proposed system is verified by the simulation results, which are performed using PSCAD/EMTDC software environment. With the proposed SFLA‐based STATCOM, the transient stability of a grid‐connected wind farm can be enhanced.

Biclustering of DNA microarray data that can mine significant patterns to help in understanding gene regulation and interactions. This is a classical multi-objective optimization problem (MOP). Recently, many researchers have developed stochastic search methods that mimic the efficient behavior of species such as ants, bees, birds and frogs, as a means to seek faster and more robust solutions to complex optimization problems. The particle swarm optimization(PSO) is a heuristics-based optimization approach simulating the movements of a bird flock finding food. The shuffled frog leaping algorithm (SFLA) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. This paper introduces SFL algorithm to solve biclustering of microarray data, and proposes a novel multi-objective shuffled frog leaping biclustering(MOSFLB) algorithm to mine coherent patterns from microarray data. Experimental results on two real datasets show that our approach can effectively find significant biclusters of high quality.

Shuffled frog leaping algorithm, a novel heuristic method, is inspired by the foraging behavior of the frog population, which has been designed by the shuffled process and the PSO framework. To increase the convergence speed and effectiveness, the currently improved versions are focused on the local search ability in PSO framework, which limited the development of SFLA. Therefore, we first propose a new scheme based on evolutionary strategy, which is accomplished by quantum evolution and eigenvector evolution. In this scheme, the frog leaping rule based on quantum evolution is achieved by two potential wells with the historical information for the local search, and eigenvector evolution is achieved by the eigenvector evolutionary operator for the global search. To test the performance of the proposed approach, the basic benchmark suites, CEC2013 and CEC2014, and a parameter optimization problem of SVM are used to compare 15 well-known algorithms. Experimental results demonstrate that the performance of the proposed algorithm is better than that of the other heuristic algorithms.

Investigations illustrate that the Internet of Things (IoT) can save costs, increase efficiency, improve quality, and provide data-driven preventative maintenance services. Intelligent sensors, dependable connectivity, and complete integration are essential for gathering real-time information. IoT develops home appliances for improved customer satisfaction, personalization, and enhanced big data analytics as a crucial Industry 4.0 enabler. Because the product design process is an important part of controlling manufacturing, there are constant attempts to improve and minimize product design time. Utilizing a hybrid algorithm, this research provides a novel method to schedule design products in production management systems to optimize energy usage and design time (combined particle optimization algorithm and shuffled frog leaping algorithm). The issue with particle optimization algorithms is that they might become stuck in local optimization and take a long time to converge to global optimization. The strength of the combined frog leaping algorithm local searching has been exploited to solve these difficulties. The MATLAB programming tool is used to simulate the suggested technique. The simulation findings were examined from three perspectives: energy usage, manufacturing time, and product design time. According to the findings, the recommended strategy performed better in minimizing energy use and product design time. These findings also suggest that the proposed strategy has a higher degree of convergence when discovering optimal solutions.

Landslide susceptibility analysis is beneficial information for a wide range of applications, including land use management plans. The present attempt has shed light on an efficient landslide susceptibility mapping framework that involves an adaptive neural-fuzzy inference system (ANFIS), which incorporates three metaheuristic methods including grey wolf optimization (GWO), particle swarm optimization (PSO), and shuffled frog leaping algorithm (SFLA) in the East Azerbaijan of Iran. To achieve this goal, 10 landslide occurrence-related influencing factors were pondered. A sum of 766 locations with landslide potentiality was recognized in the context of the study, and the Pearson correlation technique utilized in order to select the influencing factors in landslide models. The association between landslides and conditioning factors was also evaluated using a probability certainty factor (PCF) model. In the next phase, three data mining techniques were united with the ANFIS model, comprising ANFIS-grey wolf algorithm (ANFIS-GW), ANFIS-particle swarm optimization (ANFIS-PSO), and ANFIS-shuffled frog leaping algorithm (ANFIS-SFLA), were structured by the training dataset. Lastly, the receiver operating characteristic (ROC) and statistical procedures were utilized with the aim of validating and contrasting the predictive capability of the models. The findings of the study in terms of the Pearson correlation technique method for the importance ranking of conditioning factors in the context area uncovered that slope, aspect, normalized difference vegetation index (NDVI), and elevation have the highest impact on the occurrence of the landslide. All in all, the ANFIS-PSO model had high performance on both the training (RMSE = 0.288, MAE = 0.069, AUC = 0.89) and validation dataset (RMSE = 0.309, MAE = 0.097, AUC = 0.89), after which, the ANFIS-GWO model and the ANFIS-SFLA model demonstrated the second and third rates. Besides, the study revealed that benefiting the optimization algorithm with the proper selection of the techniques could facilitate landslide susceptibility modeling.

This paper presents a modified Shuffled Frog Leaping Algorithm (SFLA) applied to the design of water distribution networks. Generally, one of the major disadvantages of the traditional SFLA is the high number of parameters that need to be calibrated for proper operation of the algorithm. A method for calibrating these parameters is presented and applied to the design of three benchmark medium-sized networks widely known in the literature (Hanoi, New York Tunnel, and GoYang). For each of the problems, over 35,000 simulations were conducted. Then, a statistical analysis was performed, and the relative importance of each of the parameters was analyzed to achieve the best possible configuration of the modified SFLA. The main conclusion from this study is that not all of the original SFL algorithm parameters are important. Thus, the fraction of frogs in the memeplex q can be eliminated, while the other parameters (number of evolutionary steps Ns, number of memeplexes m, and number of frogs n) may be set to constant values that run optimally for all medium-sized networks. Furthermore, the modified acceleration parameter C becomes the key parameter in the calibration process, vastly improving the results provided by the original SFLA.

This paper presents a new approach for the optimal reconfiguration of radial distribution systems in the presence of reactive power compensators (RPCs). The proposed method is based on the simultaneous reconfiguration and RPC allocation for the mitigation of losses, equalizing the feeder load balancing as well as improving the voltage profile in power distribution networks. In this paper, the compensators are the capacitor bank and distribution static compensator, which is a type of distribution flexible alternating current transmission system device representative of conventional and modern RPCs. In this regard, the optimal states of the distribution system tie switches are determined taking into consideration the best size and location of the RPCs. In order to facilitate the algorithm for multiobjective search ability, the optimization problem is formulated for minimizing fuzzy performance indices. Since the optimization problem is nonlinear, using intelligent search methods such as the shuffled frog-leaping (SFL) algorithm can overcome the limitations of conventional analytic methods. In addition, to enhance the performance of the standard SFL, the fuzzy frog-leaping rule is used in this paper. This algorithm is more accurate and has an efficient convergence property compared to other intelligent search algorithms. The proposed method is validated using the IEEE 33-bus test system and a Tai-Power 11.4-kV distribution system as a real distribution network. The obtained results indicate that multiobjective simultaneous placement of RPCs along with reconfiguration can be more beneficial than separate single-objective optimization.

Centroid Mutation Embedded Shuffled Frog-Leaping Algorithm

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.

Gear drives are extremely critical elements of transmission systems. Designing gears that satisfy the application's requirements like load carrying capacity and strength whilst also being compact, lightweight, and economical is quite a challenging task. In recent times, a lot of research has been dedicated to optimizing speed reducers by making use of metaheuristic nature-inspired algorithms. The present work optimizes the weight of a spur speed reducer by employing two modified nature-inspired algorithms: Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA). Parameter optimization was carried out to select the best combination of c1, c2 and 𝜔 for the selected case study. It was found that there was a 1.1619% and 13.41% decrease in the cost function evaluation, as compared to the Crude Monte-Carlo and Stray Process methods used in the original case study, and around an 11% decrease as compared to results in published literature.

Optimal, efficient, and accurate drug stock management at hospital depots is crucial for ensuring the smooth operation of medical and operational services. Therefore, the use of machine learning is currently essential for managing drug stocks at hospital depots more optimally. This optimization process involves stages such as data collection, data pre-processing, attribute selection, data labeling, classification algorithm selection, model training, model eval_uation, and result interpretation. The data used in this research includes information on drug stocks at hospital depots with details on drug items, quantities, prices, depot origins, demand trends, and types of transactions. The aim of using these algorithms is to classify drug stock items into categories such as "sufficient," "deficient," and "excess" based on historical data patterns and relevant attributes. Model eval_uation is carried out by comparing classification results with actual data and measuring eval_uation metrics such as accuracy, precision, recall, and F1-score. It is hoped that the classification results will indicate the need for optimization in the previously implemented algorithms and provide new solutions for managing drug stocks at hospital depots. The Shuffle Frog Leaping algorithm (SFLA) implemented will help drug stock management staff identify demand patterns more optimally, efficiently, and accurately. Thus, this research has the potential to make significant contributions to optimizing drug stock management and decision-making at hospital depots, which will also positively impact the progress of hospital services.

Oilfield development planning is a complex task that involves multiple optimization objectives and constraints. Therefore, a study proposes an improved shuffled frog leaping algorithm to achieve multi-objective optimization tasks. In multi-objective problems, the fitness value of the algorithm is not adaptive to the memetic evolution, resulting in local search failures. Research is conducted on improving the shuffled frog leaping algorithm through non-dominated sorting genetic algorithm-II, memetic evolution, and traversal methods, and then verifying the effectiveness of the algorithm. The outcomes denoted that when the population was 30 and the grouping was 5, the algorithm proposed in the study had the fastest search speed and better optimization effect. The improved shuffled frog leaping algorithm had advantages in both construction period and cost compared to the shuffled frog leaping algorithm, with a construction period difference of 19 days and a cost difference of $13871. In comparative experiments with other algorithms, the average optimal solution and running time of the proposed algorithm were 0.324 and 7.2 seconds, respectively, which can quickly find the optimal solution in a short time. The algorithm proposed in the study can effectively optimize the complex objectives and constraints in oilfield development planning problems.

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.