A lévy flight-based shuffled frog-leaping algorithm and its applications for continuous optimization problems

Tang, Deyu; Jian-hui, Yang; Dong, Shoubin; Liu, Zhen

doi:10.1016/j.asoc.2016.09.002

Cited by 44 publications

(15 citation statements)

References 53 publications

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“…To study and analyze the capability of LBOLBOA in detail, LBOLBOA was compared with some conventional and advanced algorithms, including WOA [61], GWO [62], LSFLA [63], SE [64], m_SCA [65], ALCPSO [66], CGPSO [67], SCADE [68], OBLGWO [62], and BMWOA [61] on CEC2017 functions [69]. It is so crucial to validate a method considering all limitations and parameters using some numerical and mathematical methods [70][71][72][73].…”

Section: B Comparison Of Conventional and Advanced Algorithmsmentioning

confidence: 99%

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

et al. 2020

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In this research, X-ray and MRI images of patients suffering from cervical hyperextension injury are investigated. Also, radiographic images are collected from patients who suffered from trauma but without cervical hyperextension injury. The core engine algorithm of the optimized prediction model is kernel extreme learning machine (KELM), and the input data is 17 factors that may cause cervical hyperextension injury. As the optimization core, we utilized the Butterfly optimization algorithm (BOA). Up to now, few improved variants of BOA have been reported. The original BOA converges slowly and quickly falls into a locally optimal solution. An enhanced BOA based on orthogonal learning, Levy flight, and an exploitation engine is proposed in this paper to relieve these two shortcomings, which is called LBOLBOA. Orthogonal learning is utilized to construct guidance vectors for guiding agents toward the global optimum solution aiming to increase the accuracy of the solutions. Also, Levy flight and Broyden-Fletcher-Goldfarb-Shanno mechanisms are utilized to enrich the intensification propensities of BOA and stagnation avoidance. The proposed LBOLBOA is used to deal with continuous function optimization and machine learning problems, including parameter optimization of KELM. We rigorously verified this variant using a comprehensive set of the benchmark test suite and real-world dataset on cervical hyperextension injury. The results indicate that LBOLBOA can achieve improved performance in dealing with the function optimization and machine learning problems, especially the capability for prediction of cervical hyperextension injury.

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Section: B Comparison Of Conventional and Advanced Algorithmsmentioning

confidence: 99%

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

et al. 2020

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“…Whereas, the third term avoid multiple textual units, that convey the same information. The length of the summary is bounded by cardinality constraint, is discussed in equation (11).…”

Section: Summary Evaluation Criteriamentioning

confidence: 99%

“…In [11], the author has stated that SFLA is a novel meta-heuristic approach, applied in many combination problem. However in continuous optimization problem the algorithm falls in local optima.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of Modified Shuffled Frog leaping Algorithm for Multi-document Summarization Problem

Rautray

Dash

2019

IJCAI

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Due to massive growth of Web information, handling useful information has become a challenging issue in now-a-days. In the past few decades, text summarization is considered as one of the solution to obtained relevant information from extensive collection of information. In this paper, a novel approach using modified shuffled frog leaping algorithm (MSFLA) to extract the important sentence from multiple documents is presented. The effectiveness of MSFLA algorithm for summarization model is evaluated by comparing the ROUGE score and statistical analysis of the model with respect to results of other summarization models. The models are demonstrated by the simulation results over DUC datasets. In the present work, it elucidates that MSFLA based model improves the results and find advisable solution for summary extraction. Povzetek: Na bazah dokumentov je bil uporabljen nov algoritem MSFLA za generiranje povzetkov dokumentov.

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“…Jensi and Jiji proposed an enhanced particle swarm optimization with Lévy flight [ 37 ]. Tang et al proposed a new framework of shuffled frog-leaping algorithm based on the exploration and exploitation mechanism by using Lévy flight [ 38 ]. Yahya and Saka proposed a multiobjective artificial bee colony algorithm with Lévy flight and applied it to construction site layout planning [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior

Peng

Dong

Yin

2018

Computational Intelligence and Neuroscience

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Artificial fish swarm algorithm easily converges to local optimum, especially in solving the global optimization problem of multidimensional and multiextreme value functions. To overcome this drawback, a novel fish swarm algorithm (LFFSA) based on Lévy flight and firefly behavior is proposed. LFFSA incorporates the moving strategy of firefly algorithm into two behavior patterns of fish swarm, i.e., chasing behavior and preying behavior. Furthermore, Lévy flight is introduced into the searching strategy. To limit the search band, nonlinear view and step size based on dynamic parameter are considered. Finally, the proposed algorithm LFFSA is validated with several benchmark problems. Numerical results demonstrate that LFFSA has a better performance in convergence speed and optimization accuracy than the other test algorithms.

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A lévy flight-based shuffled frog-leaping algorithm and its applications for continuous optimization problems

Cited by 44 publications

References 53 publications

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

Performance analysis of Modified Shuffled Frog leaping Algorithm for Multi-document Summarization Problem

Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior

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