Firefly algorithm for discrete optimization problems: A survey

Tilahun, Surafel Luleseged; Ngnotchouye, Jean Medard T.

doi:10.1007/s12205-017-1501-1

Cited by 74 publications

(34 citation statements)

References 52 publications

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“…Optimization based on fireflies is one of the newest heuristics inspired by natural behaviors for optimization problems. For the work of [5] [6] [7], we know that fireflies possess an unmistakable characteristic glow, and even people who have not seen one in their life know that they emanate a light.…”

Section: B Optimization Based On Firefliesmentioning

confidence: 99%

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Applying FireFly Algorithm to Solve the Problem of Balancing Curricula

Rubio¹,

Vidal-Silva²,

Soto³

et al. 2019

ijacsa

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The problem of assigning a balanced academic curriculum to academic periods of a curriculum, that is, the balancing curricula, represents a traditional challenge for every educational institution which look for a match among students and professors. This article proposes a solution for the balancing curricula problem using an optimization technique based on the attraction of fireflies (FA) meta-heuristic. We perform a set of test and real instances to measure the performance of our solution proposal just looking to deliver a system that will simplify the process of designing a curricular network in higher education institutions. The obtained results show that our solution achieves a fairly fast convergence and finds the optimum known in most of the tests carried out.

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Section: B Optimization Based On Firefliesmentioning

confidence: 99%

“…According to the work of [5] and [6], three essential properties of the behavior of the FA are identified:…”

Section: B Optimization Based On Firefliesmentioning

confidence: 99%

Applying FireFly Algorithm to Solve the Problem of Balancing Curricula

Rubio¹,

Vidal-Silva²,

Soto³

et al. 2019

ijacsa

View full text Add to dashboard Cite

show abstract

“…The FA is a well-known swarm optimizer that was devised by Yang in 2009 for tackling unconstrained optimization challenges [46]. By examining the literature, it can be seen that the FA is able to tackle many real-life NP hard problems [28,50,52,89,90].…”

Section: Fa's Search Equationmentioning

confidence: 99%

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

et al. 2019

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The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation-exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

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“…Yang and He provided another review from a different perspective in 2013 [76]. More recently, Tilahun et al provided an updated review on the continuous versions of the firefly algorithm and its variants [67], and the discrete versions of the firefly algorithms were also reviewed by Tilahun and Ngnotchouye in 2017 [66].…”

Section: Variants Of Famentioning

confidence: 99%