A Novel Quantum Firefly Algorithm for Global Optimization

2021 ◽  
Author(s):  
Farouq Zitouni ◽  
Saad Harous ◽  
Ramdane Maamri
Keyword(s):  
Global Optimization ◽  
Firefly Algorithm

Catechize Global Optimization through Leading Edge Firefly Based Zone Routing Protocol

2020 ◽  
Vol 13 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Neha Sharma ◽  
Sherin Zafar ◽  
Usha Batra
Keyword(s):  
Global Optimization ◽  
Routing Protocol ◽  
Firefly Algorithm ◽  
Leading Edge ◽  
Packet Delivery Ratio ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Zone Routing Protocol ◽  
End To End Delay ◽  
End To End

Background: Zone Routing Protocol is evolving as an efficient hybrid routing protocol with an extremely high potentiality owing to the integration of two radically different schemes, proactive and reactive in such a way that a balance between control overhead and latency is achieved. Its performance is impacted by various network conditions such as zone radius, network size, mobility, etc. Objective: The research work described in this paper focuses on improving the performance of zone routing protocol by reducing the amount of reactive traffic which is primarily responsible for degraded network performance in case of large networks. The usage of route aggregation approach helps in reducing the routing overhead and also help achieve performance optimization. Methods: The performance of proposed protocol is assessed under varying node size and mobility. Further applied is the firefly algorithm which aims to achieve global optimization that is quite difficult to achieve due to non-linearity of functions and multimodality of algorithms. For performance evaluation a set of benchmark functions are being adopted like, packet delivery ratio and end-to-end delay to validate the proposed approach. Results: Simulation results depict better performance of leading edge firefly algorithm when compared to zone routing protocol and route aggregation based zone routing protocol. The proposed leading edge FRA-ZRP approach shows major improvement between ZRP and FRA-ZRP in Packet Delivery Ratio. FRA-ZRP outperforms traditional ZRP and RA-ZRP even in terms of End to End Delay by reducing the delay and gaining a substantial QOS improvement. Conclusion: The achievement of proposed approach can be credited to the formation on zone head and attainment of route from the head hence reduced queuing of data packets due to control packets, by adopting FRA-ZRP approach. The routing optimized zone routing protocol using Route aggregation approach and FRA augments the QoS, which is the most crucial parameter for routing performance enhancement of MANET.

scholarly journals Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization

2020 ◽  
Vol 10 (24) ◽  
pp. 8961
Author(s):  
Peng-Yeng Yin ◽  
Po-Yen Chen ◽  
Ying-Chieh Wei ◽  
Rong-Fuh Day
Keyword(s):  
Global Optimization ◽  
Firefly Algorithm ◽  
State Of The Art ◽  
Pattern Search ◽  
Experimental Result ◽  
Adaptive Memory ◽  
Glowworm Swarm Optimization ◽  
Metaheuristic Methods ◽  
Swarming Behavior ◽  
Better Than

Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.

scholarly journals A Switch-Mode Firefly Algorithm for Global Optimization

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 54177-54184 ◽  
Author(s):  
Jian Huang ◽  
Xiaochao Chen ◽  
Dongrui Wu
Keyword(s):  
Global Optimization ◽  
Firefly Algorithm ◽  
Switch Mode

scholarly journals Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2705
Author(s):  
Nebojsa Bacanin ◽  
Ruxandra Stoean ◽  
Miodrag Zivkovic ◽  
Aleksandar Petrovic ◽  
Tarik A. Rashid ◽  
...  
Keyword(s):  
Neural Networks ◽  
Global Optimization ◽  
Deep Learning ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Wide Spectrum ◽  
Statistical Tests ◽  
Dropout Rate ◽  
Original Method ◽  
Benchmark Datasets

Swarm intelligence techniques have been created to respond to theoretical and practical global optimization problems. This paper puts forward an enhanced version of the firefly algorithm that corrects the acknowledged drawbacks of the original method, by an explicit exploration mechanism and a chaotic local search strategy. The resulting augmented approach was theoretically tested on two sets of bound-constrained benchmark functions from the CEC suites and practically validated for automatically selecting the optimal dropout rate for the regularization of deep neural networks. Despite their successful applications in a wide spectrum of different fields, one important problem that deep learning algorithms face is overfitting. The traditional way of preventing overfitting is to apply regularization; the first option in this sense is the choice of an adequate value for the dropout parameter. In order to demonstrate its ability in finding an optimal dropout rate, the boosted version of the firefly algorithm has been validated for the deep learning subfield of convolutional neural networks, with respect to five standard benchmark datasets for image processing: MNIST, Fashion-MNIST, Semeion, USPS and CIFAR-10. The performance of the proposed approach in both types of experiments was compared with other recent state-of-the-art methods. To prove that there are significant improvements in results, statistical tests were conducted. Based on the experimental data, it can be concluded that the proposed algorithm clearly outperforms other approaches.

Integrating firefly algorithm with density functional theory for global optimization of Al42− clusters

2020 ◽  
Vol 139 (2) ◽  
Author(s):  
Arka Mitra ◽  
Gourhari Jana ◽  
Prachi Agrawal ◽  
Shamik Sural ◽  
Pratim K. Chattaraj
Keyword(s):  
Density Functional Theory ◽  
Global Optimization ◽  
Density Functional ◽  
Firefly Algorithm ◽  
Functional Theory

scholarly journals A Novel Hybrid Firefly Algorithm for Global Optimization

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0163230 ◽  
Author(s):  
Lina Zhang ◽  
Liqiang Liu ◽  
Xin-She Yang ◽  
Yuntao Dai
Keyword(s):  
Global Optimization ◽  
Firefly Algorithm ◽  
Hybrid Firefly Algorithm

scholarly journals A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 494 ◽  
Author(s):  
Guocheng Li ◽  
Pei Liu ◽  
Chengyi Le ◽  
Benda Zhou
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Hybrid Algorithm ◽  
Firefly Algorithm ◽  
Entropy Method ◽  
Cross Entropy ◽  
Local Optimum ◽  
Cross Entropy Method ◽  
The Cross ◽  
Searching Ability

Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algorithm. With adaptive smoothing and co-evolution, the proposed method fully absorbs the ergodicity, adaptability and robustness of the cross-entropy method. The new hybrid algorithm achieves an effective balance between exploration and exploitation to avoid falling into a local optimum, enhance its global searching ability, and improve its convergence rate. The results of numeral experiments show that the new hybrid algorithm possesses more powerful global search capacity, higher optimization precision, and stronger robustness.

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