A simple diversity guided firefly algorithm

Yu, Shaoming; Su, Shoubao; Huang, Li

doi:10.1108/k-11-2013-0241

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…In some of the modifications, a number of new algorithm parameters are introduced and tuning this parameter by itself needs another study so they are not included in the simulation. The modified versions used for simulation include Firefly Algorithm 1 [32], FFA2, [52], FFA3 [53], FFA4 [26,57], FFA5 [24,59], FFA6 [58] FFA7 [60], FFA8 [61,62], FFA9 [69], FFA10 [63] where x i -gbest is replaced by gbest-x i , FFA11 [110], FFA12 [72][73][74], FFA13 [75][76][77][78][79], FFA14 from [70].…”

Section: Simulation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

Khan¹,

Hamadneh²,

Tilahun³

et al. 2016

Optimization Algorithms - Methods and Applications

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Firefly algorithm is one of the well-known swarm-based algorithms which gained popularity within a short time and has different applications. It is easy to understand and implement. The existing studies show that it is prone to premature convergence and suggest the relaxation of having constant parameters. To boost the performance of the algorithm, different modifications are done by several researchers. In this chapter, we will review these modifications done on the standard firefly algorithm based on parameter modification, modified search strategy and change the solution space to make the search easy using different probability distributions. The modifications are done for continuous as well as non-continuous problems. Different studies including hybridization of firefly algorithm with other algorithms, extended firefly algorithm for multiobjective as well as multilevel optimization problems, for dynamic problems, constraint handling and convergence study will also be briefly reviewed. A simulationbased comparison will also be provided to analyse the performance of the standard as well as the modified versions of the algorithm. . IntroductionAn optimization problem refers to the maximization or minimization of an objective function by setting suitable values for the variables from a set of feasible values. These problems appear not only in complex scientific studies but also in our day-to-day activities. For instance, when a person wants to go from one place to another and has multiple possible routes, a decision needs to be made on which route to take. The decision can be with the objective to minimize travel time, fuel consumption and so on. However, these kinds of problems with fewer number of alternatives can easily be solved by looking at the outcome of each of the alternatives. However, in real problems, it is not always the case to have a finite and small number of alternatives. Hence, different solution methods are proposed based on the behaviour of the problem. Firefly algorithm is among those metaheuristic algorithms which have different applications. Its uncomplicated and easy steps with its effectiveness attract researchers from different disciplines it. Different studies have been performed to modify the standard firefly algorithm to boost its performance and to make it suitable for a problem at hand. In this chapter, a comprehensive study will be presented on firefly algorithm and its modified versions. A brief discussion on extended firefly algorithm with other relevant studies will also be provided. In the next section, a discussion on optimization problems with their solution methods will be given followed by a review on studies on firefly algorithm, which includes a discussion on the standard firefly algorithm with its modified versions and other relevant studies on firefly algorithm, in Section . In Section , a comparative study based on simulation results will be presented followed by summary of the chapter in Section . . Optimization problemsDecision-making problems can be found ...

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Section: Simulation Resultsmentioning

confidence: 99%

“…It is a good idea in which the best solution will not be lost through iteration. To increase the diversity of the solution, an effective modification is proposed in [110]. Using such kind of modification, the diversity of the solutions will be preserved, and the exploration behaviour of the algorithm will be improved.…”

Section: Discussionmentioning

confidence: 99%

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

Khan¹,

Hamadneh²,

Tilahun³

et al. 2016

Optimization Algorithms - Methods and Applications

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“…Besides, a new updating formula was proposed. In [33], the worst firefly is constructed by the best firefly or opposition-based learning which is determined by a random value p. To increase the diversity of population, Yu et al [34] added rand noise based on the original movement equation. In [35], firefly X i moves to the brighter firefly via chaotic sequence.…”

Section: Related Workmentioning

confidence: 99%

Firefly Algorithm With Luciferase Inhibition Mechanism

Peng

Deng

et al. 2019

IEEE Access

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Firefly algorithm (FA) was proposed by Yang which inspired from communication between fireflies through flash. As an efficient swarm intelligence algorithm, FA has been successfully applied in real-world applications. However, FA employs full attraction model that the method of selecting fireflies is sequential selection, namely each firefly can be attracted to other all fireflies in the worst case. This causes FA is prone to fall into local optima and high computational complexity. Inspired by the principle of firefly glow, an enhanced FA variant is proposed, namely, firefly algorithm with luciferase inhibition mechanism (LiFA), where luciferase inhibition mechanism is introduced to improve the effectiveness of selection. Besides, adaptive attraction model is also proposed to reduce the computational complexity and balance exploration and exploitation. Experiments are conducted on CEC2013 test suite to verify the performance of LiFA. The results show that LiFA has the best performance in some complex functions when compared other advanced FA variants.

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“…The algorithms are aimed at minimizing an objective function which is defined as follows. (6) In this paper, we apply NTSFA to integration into k-means clustering for mouse dataset. All the experimental setting is implemented according to the literature [32].…”

Section: Ntsfa Performance On a Real-world Problemmentioning

confidence: 99%

“…Similar to other metaheuristic search algorithms, firefly algorithm tends to suffer from the premature convergence issue, which is primarily due to the fast convergence feature and diversity loss of the fireflies' population during the search process. In recent years, many researchers focused on developing new firefly algorithm variants that avoid the premature convergence problem [3][4][5][6][7][8][9][10][11][12]. These variants of the FA can be stated in four areas: parameter tuning and parameter control, algorithms use of learning strategies, hybridization with other search techniques and discrete FA variants.…”

Section: Introductionmentioning

confidence: 99%

An Improved Firefly Algorithm Based on Nonlinear Time-varying Step-size

Yu¹,

Zhu²,

Zhou³

2016

IJHIT

Self Cite

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Firefly algorithm (FA) is a novel population-based stochastic optimization algorithm and has been shown to yield good performance for solving varieties of optimization problems. Meanwhile, it sustains premature convergence because it is easily to fall into the local optima which may generate a low accuracy of solution or even fail. To overcome this defect, a nonlinear time-varying step strategy for firefly algorithm (NTSFA) is presented. It uses a nonlinear decreasing and time-varying step-size for fireflies to better balance the algorithm's ability of exploration and exploitation. Numerical simulation on 20 test benchmark functions display that the proposed algorithm can increase the accuracy of the original FA. Finally, we apply NTSFA to integrate into k-means clustering for mouse dataset. The results show that NTSFA is an effective optimization algorithm.

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A simple diversity guided firefly algorithm

Cited by 9 publications

References 16 publications

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

Firefly Algorithm With Luciferase Inhibition Mechanism

An Improved Firefly Algorithm Based on Nonlinear Time-varying Step-size

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