An enhanced firefly algorithm for function optimisation problems

Lin, Xiaoyu; Zhong, Yiwen; Zhang, Hui

doi:10.1504/ijmic.2013.052298

Cited by 18 publications

(8 citation statements)

References 12 publications

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“…In addition to γ updating, β has also been done based on minimum and final values [39,40], depending on the location of the solution [31] and the light intensity of solutions [32]. In addition, different approaches are used to measure the distance between the fireflies [31,41,45]. Modifying the feasible region should be considered as a very big step length that may take the solution far away from the brighter solution and possibly out of the feasible region.…”

Section: Discussionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

“…For a small change in the distance between two fireflies results in a quick decrease of the attraction term. To deal with this problem, Lin et al [31] introduced a virtual distance which will put r in between 0 and 1 and is defined by…”

Section: B Modifying the Attraction Parametersmentioning

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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“…Lin et al [46] introduce concept of virtual distance to overcome the drastic change in attraction while there was a small decrease in distance between two fireflies. Virtual distance 'r' (between 0 to 1) was defined as…”

Section: Iv1 Random Movement Parameter Modificationmentioning

confidence: 99%

A Systematic Review on Firefly Algorithm: Past, Present, and Future

Verma¹

2020

Preprint

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“…Several variants of the firefly algorithm do already exist in the literature. Improvements have been made to FA aiming to accelerate convergence (see, for example, [28,34,42]). A Gaussian FA [22] and a hybrid FA with harmony search is proposed in [24].…”

Section: Introductionmentioning

confidence: 99%

Firefly penalty-based algorithm for bound constrained mixed-integer nonlinear programming

et al. 2016

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In this article, we aim to extend the firefly algorithm (FA) to solve bound constrained mixedinteger nonlinear programming (MINLP) problems. An exact penalty continuous formulation of the MINLP problem is used. The continuous penalty problem comes out by relaxing the integrality constraints and by adding a penalty term to the objective function that aims to penalize integrality constraint violation. Two penalty terms are proposed, one is based on the hyperbolic tangent function and the other on the inverse hyperbolic sine function. We prove that both penalties can be used to define the continuous penalty problem, in the sense that it is equivalent to the MINLP problem. The solutions of the penalty problem are obtained using a variant of the metaheuristic FA for global optimization. Numerical experiments are given on a set of benchmark problems aiming to analyze the quality of the obtained solutions and the convergence speed. We show that the firefly penalty-based algorithm compares favorably with the penalty algorithm when the deterministic DIRECT or the simulated annealing solvers are invoked, in terms of convergence speed.

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An enhanced firefly algorithm for function optimisation problems

Cited by 18 publications

References 12 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

A Systematic Review on Firefly Algorithm: Past, Present, and Future

Firefly penalty-based algorithm for bound constrained mixed-integer nonlinear programming

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