A new improved firefly clustering algorithm for SMC-PHD filter

Tian, Mengmeng; Bo, Yuming; Chen, Zhimin; Wu, Panlong; Cong, Yang

doi:10.1016/j.asoc.2019.105840

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…Moreover, a self‐learning strategy on the best solution aims to balance exploration and exploitation. Tian et al introduced the optimal firefly to guide the search of fireflies 28 . Then, combinatorial variation strategies were used to improve the extraction of multiobjective state in filter.…”

Section: Recent Work On Famentioning

confidence: 99%

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An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Wang

Zhou

et al. 2021

Int J Intell Syst

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Firefly algorithm (FA) is a popular swarm intelligence optimization algorithm. Though FA was employed to solve various optimization problems, it still has some deficiencies, such as high complexity, slow convergence rate, and low precision of solutions. To tackle these issues, this paper proposes an efficient FA based on modified search strategy and neighborhood attraction (namely MSSNaFA). In MSSNaFA, there are four main modifications. First, a novel search strategy based on dimension differences is designed. The attractiveness in the original FA is related to the Euclidean distance, while our new method uses the differences of each dimension for two fireflies to compute the attractiveness. Then, a modified neighborhood attraction mechanism is utilized to reduce the computational complexity. When the current solution is selected, it will move to the global best solution based on the new movement strategy. Third, for each firefly, three neighborhood search operations are carried out based on a preset probability. Lastly, the step factor is adaptively adjusted in the search process. Performance validation between MSSNaFA and four other FA variants show the effectiveness of our approach.

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Section: Recent Work On Famentioning

confidence: 99%

“…Tian et al introduced the optimal firefly to guide the search of fireflies. 28 Then, combinatorial variation strategies were used to improve the extraction of multiobjective state in filter. Peng et al proposed an improved FA based on courtship learning, 29 in which fireflies were partitioned into two classes in terms of gender.…”

Section: Recent Work On Famentioning

confidence: 99%

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Wang

Zhou

et al. 2021

Int J Intell Syst

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“…Firefly algorithm is developed based on the fireflies group behavior and simulated for the optimization process. Attractiveness and light intensity are the two important elements in the firefly algorithm [19]. The optimization process is described as follows:…”

Section: Elite Firefly Algorithmmentioning

confidence: 99%

Elite Firefly and Long Short Term Memory Based Model for Texture Classification

2021

IJIES

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“…Simulation results showed the new algorithm outperformed flower pollination algorithm, differential evolution (DE) and whale optimization algorithm. Tian et al presented a modified clustering method based on FA, 35 in which FA was used to extract the states of multiple targets. Localization algorithm is important in WSN 36 .…”

Section: A Review Of Famentioning

confidence: 99%

A modified firefly algorithm based on neighborhood search

2020

Concurrency and Computation

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Firefly algorithm (FA) is a new member of intelligent optimization algorithms. Several studies demonstrated that FA could successfully deal with several engineering and theoretical optimization problems. Nevertheless, there are some weak points in FA, such as parameter dependence and high computational complexity. To overcome the above issues, this paper proposes a modified FA based on neighborhood search (MFANS). In MFANS, there are three modifications. First, a modified attraction strategy is employed to reduce the complexity. Then, a neighborhood search method is used to search around the best neighborhood solutions. Third, the step parameter is dynamically adjusted. Performance of MFANS is compared with two improved FAs. Results show the effectiveness of MFANS.

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A new improved firefly clustering algorithm for SMC-PHD filter

Cited by 11 publications

References 28 publications

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Elite Firefly and Long Short Term Memory Based Model for Texture Classification

A modified firefly algorithm based on neighborhood search

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