A mnemonic shuffled frog leaping algorithm with cooperation and mutation

Wang, Hongbo; Zhang, Ke-Peng; Tu, Xuyan

doi:10.1007/s10489-014-0642-x

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…Generally, the total population of frogs is found to be the most important parameter, proportional to the complexity of the problem, and max Di is typically 100% [21]. The number of memeplexes, m, is around 10% of n, and the number of local iterations before shuffling is generally between m/2 and m x 2 and the number of global iterations is typically from 100 to 1000 [22]. It is advisable to test a variety of parameter settings for any SFLA algorithm to find the fastest convergence.…”

Section: Optimization Methodsmentioning

confidence: 99%

A novel approach for coordinated design of TCSC controller and PSS for improving dynamic stability in power systems

Alwan¹

2023

PEN

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The purpose of this article is to present a novel strategy for the coordinated design of the Thyristor Controlled Series Compensator (TCSC) controller and the Power System Stabilizer (PSS). A time domain objective function that is based on an optimization problem has been defined. This objective function takes into account not only the influence that disturbances have on the mechanical power, but also, and this is more accurately the case, the impact that disturbances have on the reference voltage. When the objective function is minimized, potential disturbances are quickly mitigated, and the deviation of the speed of the generator's rotor is limited; as a result, the system's stability is ultimately improved. Particle Swarm Optimization (PSO) and the Shuffled Frog Leaping Algorithm are both components of a composite strategy that is utilized in the process of determining the optimal controller parameters. (SFLA). An independent controller design as well as a collaborative controller design utilizing PSS and TCSC are developed, which enables a direct evaluation of the functions performed by each. The presentation of the eigenvalue analysis and the findings of the nonlinear simulation can help to provide a better understanding of the efficacy of the outcomes. The findings indicate that the coordinated design is able to successfully damp low-frequency oscillations that are caused by a variety of disturbances, such as changes in the mechanical power input and the setting of the reference voltage, and significantly enhance system stability in power systems that are connected weekly.

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Section: Optimization Methodsmentioning

confidence: 99%

A novel approach for coordinated design of TCSC controller and PSS for improving dynamic stability in power systems

Alwan¹

2023

PEN

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“…Simulation results show that the resulting fuzzy controller outperformed the LQR controller. In addition, SFLA has been applied to pattern recognition [65], function optimization [66][67][68], signal and information processing [69], and other fields [70,71].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning to Harmonize Performance of Shuffled Frog Leading Algorithm: A Solution for Optimized Photovoltaic Cells

Weng

Heidari

Chen

2023

International Journal of Energy Research

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Accurate determination of photovoltaic (PV) parameters holds immense significance for ensuring the reliability of solar system operations, uninterrupted power supply for load consumption, and efficient control and management of energy sources. I-V curves transform parameter extraction into a nonlinear optimization problem supported by the I-V data points in the PV model to characterize the PV model macroscopically. Therefore, this paper proposes a novel parameter extraction model using the Q -learning-based multistrategy improved shuffled frog leading algorithm (CRNSFLA). During the evaluation process of the proposed algorithm, the colony predation algorithm (CPA) is utilized to expand the search range of the worst individual, which is no longer confined to the line segment range between the current and best values. In the later stage of evaluation, the optimal individual serves as the starting point and is applied to the Nelder-Mead simplex (NMS) for forming a simplex to mine higher-quality solutions. Besides, the simplest reinforcement Q -learning allows for a reasonable switch between these two mechanisms. A reasonable balance between exploration and exploitation trends is ensured while making full use of the advantages of both according to the reward and punishment mechanisms. The comprehensive test results under various optimization functions, different PV models, and environmental conditions demonstrate that the proposed algorithm is more advantageous than existing algorithms for parameter extraction problems. Specifically, CRNSFLA had RMSEs of 9.8602E-04, 9.8248E-04, and 9.8248E-04 in the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM), respectively. Moreover, compared with the original shuffled frog leading algorithm, the CRNSFLA showed significant improvements in 62% of the optimization functions. Therefore, CRNSFLA can be considered an effective tool for solar cell parameter extractions.

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“…So, the centroid of three new individuals was considered for the frog leaping rule. Hong-bo Wang et al [ 42 ] combined the historical information, information of the local frog and global frog substituted for the basic frog leaping search method, and the mutation operation by the normal distribution and Cauchy distribution was used for the globally best frog and the worst frog. Liu C et al [ 43 ] used the chaotic opposition-based learning to achieve the population initialization, and then the adaptive nonlinear inertia weight and the perturbation operator strategy based on Gaussian mutation were used for the balance between the exploration and the exploitation.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications

Tang

Zhao

Jian-hui

et al. 2021

Computational Intelligence and Neuroscience

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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.

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A mnemonic shuffled frog leaping algorithm with cooperation and mutation

Cited by 13 publications

References 18 publications

A novel approach for coordinated design of TCSC controller and PSS for improving dynamic stability in power systems

A novel approach for coordinated design of TCSC controller and PSS for improving dynamic stability in power systems

Reinforcement Learning to Harmonize Performance of Shuffled Frog Leading Algorithm: A Solution for Optimized Photovoltaic Cells

An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications

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