An improved firefly algorithm based on probabilistic attraction

Yu, Gan

doi:10.1504/ijcsm.2016.081701

Cited by 22 publications

(10 citation statements)

References 14 publications

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“…To tackle these problems, several improved attraction strategies were designed. [19][20][21] Recently, a new attraction method called NA was proposed. 21 In NA, each solution in the swarm connects with two other adjacent solutions based on the indices of solutions.…”

Section: Modified Attraction Methodsmentioning

confidence: 99%

“…Yu presented a probabilistic attraction method. 20 Recently, a neighborhood attraction (NA) was constructed based on a circular population structure. 21 Experiments showed the NA method could effectively reduce the complexity and obtain excellent results.…”

Section: A Review Of Famentioning

confidence: 99%

“…[48][49][50][51] 21 Then, k is constrained in the interval. 1,9 Inspired by the study, 20 Results of MFANS on different parameter combinations are given in…”

Section: Experiments Setupmentioning

confidence: 99%

“…In RA, a solution is not attracted by the rest of all solutions, but only a randomly selected one. Yu presented a probabilistic attraction method 20 . Recently, a neighborhood attraction (NA) was constructed based on a circular population structure 21 .…”

Section: A Review Of Famentioning

confidence: 99%

“…Moreover, the computational complexity is higher than other IOAs. To tackle these problems, several improved attraction strategies were designed 19‐21 …”

Section: Modified Fa Based On Neighborhood Search (Mfans)mentioning

confidence: 99%

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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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Section: Modified Attraction Methodsmentioning

confidence: 99%

Section: A Review Of Famentioning

confidence: 99%

“…[48][49][50][51] 21 Then, k is constrained in the interval. 1,9 Inspired by the study, 20 Results of MFANS on different parameter combinations are given in…”

Section: Experiments Setupmentioning

confidence: 99%

Section: A Review Of Famentioning

confidence: 99%

“…Moreover, the computational complexity is higher than other IOAs. To tackle these problems, several improved attraction strategies were designed 19‐21 …”

Section: Modified Fa Based On Neighborhood Search (Mfans)mentioning

confidence: 99%

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A modified firefly algorithm based on neighborhood search

2020

Concurrency and Computation

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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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