The Chaos-Based Shuffled Frog Leaping Algorithm and Its Application

Long-term scheduling of large cascade hydropower stations in Jinsha River, China

A medium and long-term reservoir optimal operation model of cascade hydropower stations is established, and chaos optimization algorithm is used to optimize the medium and long-term reservoir operation of cascade hydropower stations. The calculation results show that the algorithm can solve the nonlinear cascade hydropower station reservoir optimal operation problem with complex constraints, with high accuracy and fast convergence speed. It provides an effective algorithm for solving the optimal operation of cascade hydropower station reservoir.KeywordsCascade hydropower stationsOptimal operationChaos optimization algorithm

Due to the fact that Sine Cosine Algorithm(SCA) may easily premature convergence, by taking cultural algorithm as the framework of population space and using chaotic search to lead the evolvement of belief space, a new Chaos Cultural Sine Cosine Algorithm (CCSCA) was present in this paper. And the results of typical benchmark functions show that CCSCA has better global searching ability than SCA to avoid the premature phenomenon. Finally, CCSCA was employed for optimal operation of cascade hydropower stations (OOCHS), and case study showed that CCSCA could get satisfying results with high accuracy and fast efficient, thus providing a new efficient method to solve OOCHS.

The joint optimization operation of cascade hydropower stations has excellent performance in economic and social benefits. However, the connections between hydropower and power among cascaded hydropower stations are complex, and they are affected by many factors. In order to solve the multi-stage nonlinear optimization problem, this paper presents an intelligent algorithm and calculate example. The calculation results show that the intelligent algorithm has better performance, and has great application space in solving the joint optimization problem of cascade hydropower stations.

Discussion on the monotonicity principle of the two-stage problem in joint optimal operation of cascade hydropower stations

The Resource Constrained Project Scheduling Problem (RCPSP) is been studied under different kinds of constraints and limitations. In this paper, we are going to consider the discounted cash flows for project activities, and delay penalties which occur when the project make span exceeds its deadline as the objective function of the RCPSP. To solve the model, we will take advantage of three different meta-heuristic algorithms - Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA), and Shuffled Frog Leaping Algorithm (SFLA) - to achieve the optimal solution of the problem. The evaluation of the algorithms performance reveals that, in comparison with ICA and SFLA, GA performs better, especially in large-scale 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.

Shuffled frog leaping Algorithm (SFLA) is a new memetic, population based, meta-heuristic algorithm, has emerged as one of the fast, robust with efficient global search capability. In order to enhance the algorithm’s stability and the ability to search the global optimum, the conventional SFL Algorithm has been modified in our work by using the local best value of each memeplex instead of generating a new frog, to enhance the effectiveness of the SFLA. This paper implements the application of Modified SFLA in Partitional clustering of the unlabelled data. This algorithm is applied on various classification problems and the simulated results demonstrate that, this modified SFLA has outperformed the conventional SFL Algorithm.

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.

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.

Two new hybrid algorithms are proposed to improve the performances of the meta-heuristic optimization algorithms, namely the Grey Wolf Optimizer (GWO) and Shuffled Frog Leaping Algorithm (SFLA). Firstly, it advances the hierarchy and position updating of the mathematical model of GWO, and then the SGWO algorithm is proposed based on the advantages of SFLA and GWO. It not only improves the ability of local search, but also speeds up the global convergence. Secondly, the SGWOD algorithm based on SGWO is proposed by using the benefit of differential evolution strategy. Through the experiments of the 29 benchmark functions, which are composed of the functions of unimodal, multimodal, fixed-dimension and composite multimodal, the performances of the new algorithms are better than that of GWO, SFLA and GWO-DE, and they greatly balances the exploration and exploitation. The proposed SGWO and SGWOD algorithms are also applied to the prediction model based on the neural network. Experimental results show the usefulness for forecasting the power daily load.

With the succession of river basins and inter-basin hydropower stations, the joint optimal operation of cascade hydropower stations in the river basin has large-scale, nonlinear, strong coupling, and multi-target characteristics, and must consider the effects of hydrometeorology, water demand, and power grid security. Focusing on the preparation of short-term power generation plans for cascade hydropower stations on the Qingjiang River, a comprehensive multi-objetive power generation planning model with the largest total power generation and the least load variance on the power grid is established. Based on the constraint processing method of multi-objective optimization scheduling in long-term, the optimal flow distribution technology is adopted to improve the accuracy of power generation planning. The above model is solved by using SMPSO. The results show that the improved algorithm can effectively overcome the shortcomings of slow convergence speed and easy convergence to local optimum. It can improve the power generation efficiency of the whole cascade while responding to the peaking demand of the power grid and provide a new solution to the short-term power generation planning ideas.

In order to optimize the medium and long term joint operation of cascade hydropower stations in Tibet's Lhasa River-Yarlung Zangbo River basin, this study takes year as the cycle, month and ten days as the dispatching period, and constructs the medium and long term maximum annual power generation model of Pangduo, Zhikong, Dagu, Zangmu and Jiacha cascade hydropower stations, and uses the successive approximation dynamic programming (DPSA) algorithm to solve the problem. By determining the boundary conditions such as the comprehensive output coefficient, the highest and lowest water level, the maximum and minimum output, the joint operation calculation is carried out according to 27 typical year combinations: the operation mode of Zhikong station under different typical year types and the kinetic energy index of the cascade joint operation of each hydropower station are studied. The dispatching process of each hydropower station under the maximum power generation mode is studied, and the operation proposal of the hydropower station with Pangduo and Zhikong are given.

This paper deals with the combined automatic voltage control (AVR) and automatic load frequency control (ALFC) of an unequal three-area three-source system. Area-1, area-2 comprises of thermal, diesel and hydro plant, and area3 comprises of gas, diesel and thermal plant. A maiden attempt has been made to apply a Tilt-integral-derivative (TID) as secondary controller in both AVR and ALFC loops. A nature inspired metaheuristic algorithm called shuffled frog leaping algorithm (SFLA) is successfully applied for ALFC and AVR studies. Investigations are carried out by considering 1% SLP in area-1 considering integral double derivative (IDD), proportional-ID (PID) and TID controller reveals the better responses with TID controller in terms of settling time and oscillations magnitude. Moreover, the superiority of SFLA technique is tested with cuckoo search and firefly algorithms in terms of dynamic responses and convergence characteristics. Furthermore, the system dynamic responses are compared with and without considering nonlinearities like GRC, GDB as a realistic approach. Sensitivity analysis reveals that SFLA optimized TID controller gains and parameters at nominal conditions are found to be robust against discrepancy in system loading conditions and inertia.

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