Firefly Algorithm

doi:10.1002/9780470640425.ch17

Cited by 81 publications

(4 citation statements)

References 5 publications

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“…The firefly algorithm (FA) is one of the most recently introduced metaheuristic, nature-inspired swarm intelligence techniques formulated by Xin-She Yang in 2008 [17]. The light emitted by fireflies is used for the attraction of fireflies for their potential mate.…”

Section: Literature Reviewmentioning

confidence: 99%

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Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms

et al. 2020

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This research work proposed a hybrid model to maximize energy consumption and maximize user comfort in residential buildings. The proposed model consists of two widely used optimization algorithms named the firefly algorithm (FA) and genetic algorithm (GA). The hybridization of two optimization approaches results in a better optimization process, leading to better performance of the process in terms of minimum power consumption and maximum occupant’s comfort. The inputs of the optimization model are illumination, temperature, and air quality from the user, in addition with the external environment. The outputs of the proposed model are the optimized values of illumination, temperature, and air quality, which are, in turn, used in computing the values of user comfort. After the computation of the comfort index, these values enter the fuzzy controllers, which are used to adjust the cooling/heating system, illumination system, and ventilation system according to the occupant’s requirement. A user-friendly environment for power consumption minimization and user comfort maximization using data from different sensors, user, processes, power control systems, and various actuators is proposed in this work. The results obtained from the hybrid model have been compared with many state-of-the-art optimization algorithms. The final results revealed that the proposed approach performed better as compared to the standard optimization techniques.

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Section: Literature Reviewmentioning

confidence: 99%

“…The firefly algorithm (FA) is a widely used metaheuristic, nature-inspired swarm intelligence technique formulated by Xin-She Yang in 2008 [17]. The significant wellspring of motivation prompting the advancement of the FA is the wonder of light emanation by fireflies.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms

et al. 2020

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“…Optimization methods are getting more attention to solve these problems, especially swarm intelligence algorithms, which have an ability in fast and detailed search of optimal solutions. Swarm intelligence algorithms are Ant Colony Optimization (ACO) (Yaralidarani & Shahverdi, 2016), Particle Swarm Optimization (PSO) (Huang et al, 2016), Artificial Bee Colony (ABC) (Chen et al, 2019), Cuckoo Search (CS) (Rajabioun, 2011), Firefly Algorithm (FA) (Yang, 2010a), Bat Algorithm (BA) (Yang, 2010b), Grey Wolf Optimizer (GWO) (Kohli & Arora, 2018), Dolphin Echolocation (DE) (Kaveh & Farhoudi, 2013), Whale Optimization Algorithm (WOA) (Kaur & Arora, 2018), Fruitfly Optimization Algorithm (FOA) (Pan, 2012), and others.…”

Section: Introductionmentioning

confidence: 99%

Development of modified cooperative particle swarm optimization with inertia weight for feature selection

Samigulina

Massimkanova

2020

Cogent Engineering

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“…Some of the most popular metaheuristic algorithms that have been introduced by researchers so far are as follows. The genetic algorithm (GA) [4], the simulated annealing (SA) algorithm [5], the tabu search (TS) algorithm [6], the ant colony optimization (ACO) algorithm [7], the particle swarm optimization (PSO) algorithm [8], the differential evolution (DE) algorithm [9], the harmony search (HS) algorithm [10], the monkey search (MS) algorithm [11], the ABC algorithm [12], the firefly algorithm (FA) [13], the intelligent water drops (IWD) algorithm [14], the cuckoo search (CS) algorithm [15,16], the bat algorithm (BA) [17,18] and the MBO algorithm [19]. In parallel with these studies many researchers have developed new methodologies based on the existing algorithms, such as modified hybrid forms [20][21][22][23][24][25][26][27][28][29][30][31][32] and parallel running methods [33][34][35][36][37][38], with the aim of getting better optimization performances.…”

Section: Introductionmentioning

confidence: 99%

Balancing exploration and exploitation by using sequential execution cooperation between artificial bee colony and migrating birds optimization algorithms

Makas¹,

Yumuşak²

2016

Turk J Elec Eng & Comp Sci

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The artificial bee colony (ABC) algorithm is a metaheuristic search method inspired by bees' foraging behaviour. With its global search ability in scout bee phase, it can easily escape from local optimum traps in the problem space. Therefore, it is good at exploration. The migrating birds optimization (MBO) algorithm is another recent metaheuristic search method. It simulates birds' V flight formation, which minimizes energy consumption during flight. The MBO algorithm achieves a good convergence to the global optimum by using its own unique benefit mechanism. That is, it has a good exploitation capability. This paper aimed to combine the good exploration property of the ABC algorithm and the good exploitation property of the MBO algorithm via a sequential execution strategy. In the proposed method, firstly, the ABC algorithm runs. This enables solutions to escape from local optimum traps and orientates them to the region in which the global optimum exists. Then the MBO algorithm runs. It performs a good convergence to the global optimum. In the proposed method, some variants of the ABC algorithm and some other well-known optimization algorithms were tested via benchmark functions. It was seen in the experiments that the proposed method gave competitive benchmark test results considering both success rates and convergence performances.

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Firefly Algorithm

Cited by 81 publications

References 5 publications

Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms

Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms

Development of modified cooperative particle swarm optimization with inertia weight for feature selection

Balancing exploration and exploitation by using sequential execution cooperation between artificial bee colony and migrating birds optimization algorithms

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