Shuffled Frog Leaping(洗牌的青蛙跳跃)研究综述
Shuffled Frog Leaping 洗牌的青蛙跳跃 - In this respect, an MPPT technique, augmented by the incremental conductance (INC) and hybrid shuffled frog-leaping and pattern search algorithm (HSFLA-PS) based adaptive neuro-fuzzy inference system (ANFIS) has been presented in this paper for the solar PV systems applications. [1] Eventually, the meta-heuristic methods of Genetic and shuffled frog-leaping algorithms are exploited to solve resulting PLC channel allocation problem via minimizing the interference. [2] For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. [3] Simulation results show that the shown algorithm can achieve a higher point coverage rate over genetic quantum algorithm (QGA) and Shuffled Frog-Leaping Algorithm (SFLA). [4] This paper proposes a hybrid approach which combines Improved Shuffled Frog-Leaping Algorithm (ISFLA) with Dynamic Programming (DP) for disassembly process planning (DPP). [5] The optimization problem is solved by resorting to two nature-inspired algorithms, namely the gray wolf optimizer (GWO) and the shuffled frog-leaping algorithm (SFLA). [6] In this study, a new differentiated shuffled frog-leaping algorithm (DSFLA) is presented to solve the problem with makespan minimization. [7] The SOS algorithm results were compared to the artificial bee colony, imperialist competitive, moth swarm, shuffled frog-leaping, genetic algorithm, and particle swarm optimization algorithms. [8] Shuffled frog-leaping algorithm (SFLA) is a population-based cooperative search technique containing virtual interactive frogs distributed into multiple memeplexes. [9] To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a Shuffled Frog-Leaping Algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. [10] In this paper, distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with fuzzy processing time is considered and a cooperated shuffled frog-leaping algorithm (CSFLA) is presented to optimize fuzzy makespan, total agreement index and fuzzy total energy consumption simultaneously. [11] This paper proposes a novel improved shuffled frog-leaping algorithm (ISFLA) to address this problem. [12] In order to demonstrate the fitting precision of the different models, the shuffled frog-leaping algorithm (SFLA) is employed to identify the parameters of MR damper models. [13] Aiming at the flow shop scheduling problem with limited buffer, a hybrid shuffled frog-leaping algorithm (HSFLA) combining variable neighborhood search (VNS) and frog-leaping algorithm (SFLA) is proposed to minimize the makespan. [14] In specific, both relatively older and well established, as well as newer but promising methods are included, namely Differential Evolutionary, Memetic Algorithm, Imperialist Competitive Algorithm, Biogeography-Based Optimization algorithm, Teaching-Learning-Based optimization, Sheep Flock Heredity algorithm, Shuffled Frog-Leaping algorithm, and Bacteria Foraging Optimization algorithm. [15] The Shuffled frog-leaping algorithm is a method of this type that has been applied to solve different types of problems. [16] 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. [17] In the approach proposed in this study, a Genetic Algorithm (GA) handles the feature selection task, while a Shuffled Frog-Leaping Algorithm (SFLA) handles the clustering task. [18] Accordingly, this study suggests two artificial intelligent techniques considered as search algorithms, the differential evolution (DE) algorithm and modified shuffled frog-leaping algorithm (MSFLA). [19] At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog-leaping algorithm (MSFLA) for the accurate classifying machinery fault method. [20] Experimental results show that GHMS-RCS outperforms standard HMS as well as other population-based algorithms including particle swarm optimisation (PSO), shuffled frog-leaping algorithm (SFLA), and biogeography-based optimisation (BBO). [21] This paper utilizes three algorithms, namely shuffled frog-leaping algorithm (SFLA), firefly optimization algorithm (FOA), and imperialist competitive algorithm (ICA) for the PEMFC model calibration. [22] In this paper, a multi-objective bio-inspired algorithm based on the Firefly and the Shuffled frog-leaping algorithms is presented as a clustering-based routing protocol for Wireless Sensor Networks. [23] The proposed model incorporates a multi-objective optimization-based methodology that employs three evolutionary optimization algorithms to calculate the optimum thresholds which are: (1) genetic algorithm, (2) particle swarm optimization algorithm, and (3) shuffled frog-leaping algorithm. [24] 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. [25] A novel metaheuristic algorithm SDE augments the features both shuffled frog-leaping algorithm and differential evolution algorithm by employing partitioning and shuffling. [26] In order to overcome the shortcoming, this article suggests a Shuffled Frog-leaping Differential Evolution (abbreviation for SFDE) algorithm in a cognitive radio network, which combines Differential Evolution with Shuffled Frog Leaping Algorithm. [27] 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. [28] Several and well-known computing methods are documented in the literature for solving constrained complicated nonlinear functions, where in this study a shuffled frog-leaping algorithm (SFLA) is suggested, which is one of the artificial intelligence techniques and regarded as a search method. [29] To optimize the parameters of classifiers, a continuous shuffled frog-leaping algorithm is applied. [30]在这方面，本文提出了一种 MPPT 技术，该技术由增量电导 (INC) 和混合洗牌蛙跳和模式搜索算法 (HSFLA-PS) 基于自适应神经模糊推理系统 (ANFIS) 增强，用于太阳能光伏系统应用。 [1] 最后，利用遗传的元启发式方法和洗牌蛙跳算法通过最小化干扰来解决由此产生的PLC信道分配问题。 [2] 为此，我们应用了洗牌蛙跳算法（SFLA）并提出了一个合适的多目标适应度函数。 [3] 仿真结果表明，与遗传量子算法（QGA）和Shuffled Frog-Leaping算法（SFLA）相比，所提出的算法可以实现更高的点覆盖率。 [4] 本文提出了一种混合方法，该方法将改进的 Shuffled Frog-Leaping 算法 (ISFLA) 与动态规划 (DP) 相结合，用于拆卸过程规划 (DPP)。 [5] 优化问题通过采用两种自然启发的算法来解决，即灰狼优化器（GWO）和洗牌蛙跳算法（SFLA）。 [6] 在这项研究中，提出了一种新的差分洗牌蛙跳算法（DSFLA）来解决最小化制造时间问题。 [7] 将 SOS 算法结果与人工蜂群、帝国主义竞争、飞蛾群、洗牌蛙跳、遗传算法和粒子群优化算法进行了比较。 [8] 洗牌青蛙跳跃算法 (SFLA) 是一种基于种群的合作搜索技术，包含分布在多个 memeplexes 中的虚拟交互式青蛙。 [9] 为此，将基于人工神经网络 (ANN) 和混洗蛙跳算法 (SFLA) 集成的混合方法应用于这些测量产生的数据，用于来自传感器的数据融合，称为 SFLA-ANN . [10] 本文考虑具有模糊处理时间的分布式节能混合流水车间调度问题（DEHFSP），提出了一种协同混洗蛙跳算法（CSFLA）来同时优化模糊制造期、总协议指数和模糊总能耗。 [11] 本文提出了一种新的改进的洗牌蛙跳算法（ISFLA）来解决这个问题。 [12] 为了证明不同模型的拟合精度，采用混洗蛙跳算法（SFLA）来识别MR阻尼器模型的参数。 [13] nan [14] nan [15] nan [16] nan [17] nan [18] nan [19] nan [20] nan [21] nan [22] nan [23] nan [24] nan [25] nan [26] nan [27] nan [28] nan [29] nan [30]