Evaluation performance study of Firefly algorithm, particle swarm optimization and artificial bee colony algorithm for non-linear mathematical optimization functions

Agarwal, Sonali; Singh, Amrit Pal; Anand, Nitin

doi:10.1109/icccnt.2013.6726474

Cited by 18 publications

(10 citation statements)

References 4 publications

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“…It also has the capability to deal with several multi optimization problems and are highly non-linear [41]. Here, the firefly with high or low intensity gets attracted with the neighbouring firefly having high or low intensity [18]. Let us consider, XY D be the distance among two fireflies namely Y and X .…”

Section: B Light Intensity Variation and Attraction Capabilitymentioning

confidence: 99%

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CSCF: a chaotic sine cosine firefly algorithm for practical application problems

Hassan

2020

Neural Comput & Applic

122

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Recently, numerous meta-heuristic based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity etc. However, several optimization algorithms namely firefly algorithm, sine cosine algorithm, particle swarm optimization algorithm have few drawbacks such as computational complexity, convergence speed etc. So to overcome such shortcomings, this paper aims in developing a novel Chaotic Sine Cosine Firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine cosine algorithm (SCA) and the Firefly (FF) algorithms are integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping is selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm.

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Section: B Light Intensity Variation and Attraction Capabilitymentioning

confidence: 99%

“…The standard firefly optimization algorithm generates the random position; whereas the chaotic firefly generates according to the chaotic maps. (18) From equation 18, in standard sine cosine algorithm, 1 R is created randomly between 0 and 1; while in variant III, it is a chaotic number between 0 and 1.…”

Section: ➢ Variant-iimentioning

confidence: 99%

CSCF: a chaotic sine cosine firefly algorithm for practical application problems

Hassan

2020

Neural Comput & Applic

122

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“…Equation ( 11) is applied for bringing up the succeeding step of firefly's as demonstrated in Figure 3. 63…”

Section: Svmmentioning

confidence: 99%

Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Band

Ghazvinei

Yusof

et al. 2020

Energy Science & Engineering

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Theoretically, the ocean has the potential to generate 20,000 TWh to 92 000 TWh of electricity. While as of 2012, our planet only requires 16 000 TWh of electricity. 1,2 A large portion of the marine energy continues to be unexploited because of the high cost involved in generating electricity. 3,4 Tidal current or tidal stream technologies had great strides improvement on the way to commercialization in the last decade. Tidal range technologies utilize a barrage to produce power from the elevation variance among low and high tide. The innovations made for tidal range power generation are tidal "reefs," "lagoons," and "fences," as well as low head tidal barrages. 5 There are three groups of tidal energy technologies:

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“…The known advantage of FA over the existing classical optimization method is its fast convergence speed [13]. As stated in [14] and [15], it has a better performance compared to other popular optimization algorithms such as particle swarm optimization and artificial bee colony. Firefly algorithm also has other advantages when solving problems; the solution or the attractiveness of the firefly is not gender specific.…”

Section: Algorithm For Weakest Line Identificationmentioning

confidence: 99%

Active and Reactive Power Scheduling Optimization using Firefly Algorithm to Improve Voltage Stability under Load Demand Variation

Zamani¹,

Musirin²,

Hassan³

et al. 2018

IJEECS

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This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.<span style="font-size: 9pt; font-family: Arial, sans-serif;" lang="EN-US">This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.</span>

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Evaluation performance study of Firefly algorithm, particle swarm optimization and artificial bee colony algorithm for non-linear mathematical optimization functions

Cited by 18 publications

References 4 publications

CSCF: a chaotic sine cosine firefly algorithm for practical application problems

CSCF: a chaotic sine cosine firefly algorithm for practical application problems

Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Active and Reactive Power Scheduling Optimization using Firefly Algorithm to Improve Voltage Stability under Load Demand Variation

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