Modified Approach of Firefly Algorithm for Non-Minimum Phase Systems

Meena, S.; Chitra, K.

doi:10.17485/ijst/2015/v8i23/72264

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…Hence, γ or β should not be updated at the same time. 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].…”

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%

“…In this chapter, β is modified using β = (β max − β min )e −γr 2 + β min , where β min and β max are user-supplied values. Similar modification is also done in [40].…”

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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“…The performance of the FA is assessed by measuring the attraction between individual fireflies, and different settings of the randomization parameter α and attractiveness coefficient β 0 parameters can result in different performances. To address some of these issues, modifications were proposed to the standard firefly algorithm in [62]. These modifications update the brightness of the fireflies to obtain a global solution and prevent the distance between individual fireflies from becoming too large.…”

Section: Firefly Algorithmmentioning

confidence: 99%

Optimal Coordination of Directional Overcurrent Relays Using Hybrid Firefly–Genetic Algorithm

et al. 2023

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The application of directional overcurrent relays (DOCRs) plays an important role in protecting power systems and ensuring their safe, reliable, and efficient operation. However, coordinating DOCRs involves solving a highly constrained and nonlinear optimization problem. The primary objective of optimization is to minimize the total operating time of DOCRs by determining the optimal values for decision variables such as the time multiplier setting (TMS) and plug setting (PS). This article presents an efficient hybrid optimization algorithm that combines the modified firefly algorithm and genetic algorithm to achieve improved solutions. First, this study modifies the firefly algorithm to obtain a global solution by updating the firefly’s brightness and to prevent the distance between the individual fireflies from being too far. Additionally, the randomized movements are controlled to produce a high convergence rate. Second, the optimization problem is solved using the genetic algorithm. Finally, the solution obtained from the modified firefly algorithm is used as the initial population for the genetic algorithm. The proposed algorithms have been tested on the IEEE 3-bus, 8-bus, 9-bus and 15-bus networks. The results indicate the effectiveness and superiority of the proposed algorithms in minimizing the total operating time of DOCRs compared with other optimization methods presented in the literature.

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“…β was calculated using β = (βmax − βmin) e −γr 2 + βmin [51,52], where βmin and βmax were user driven parameters.…”

Section: Iv1 Random Movement Parameter Modificationmentioning

confidence: 99%