A hybrid multi-objective firefly algorithm for big data optimization

Wang, Hui; Wang, Wenjun; Cui, Laizhong; Sun, Haoyu; Zhao, Jia; Wang, Yun; Xue, Yu

doi:10.1016/j.asoc.2017.06.029

Cited by 115 publications

(56 citation statements)

References 55 publications

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“…ere are numerous heuristic algorithms in the literature, including cuckoo search algorithm [31,32], firefly algorithm [33], teaching-learning-based optimization [34], biogeography-based optimization [35], and so on. We choose PSO because it is the most well-known and classic swarm intelligent optimization algorithm, and it has been successfully applied to near all engineering domains.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

An Efficient Approximation for Nakagami-mQuantile Function Based on Generalized Opposition-Based Quantum Salp Swarm Algorithm

Gao

Hou

Zhang

et al. 2019

Mathematical Problems in Engineering

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With the further research in communication systems, especially in wireless communication systems, a statistical model called Nakagami-mdistribution appears to have better performance than other distributions, including Rice and Rayleigh, in explaining received faded envelopes. Therefore, the Nakagami-mquantile function plays an important role in numerical calculations and theoretical analyses for wireless communication systems. However, it is quite difficult to operate numerical calculations and theoretical analyses because Nakagami-mquantile function has no exact closed-form expression. In order to obtain the closed-form expression that is able to fit the curve of Nakagami-mquantile function as well as possible, we adopt the method of curve fitting in this paper. An efficient expression for approximating the Nakagami-mquantile function is proposed first and then a novel heuristic optimization algorithm—generalized opposition-based quantum salp swarm algorithm (GO-QSSA)—which contains quantum computation, intelligence inspired by salp swarm and generalized opposition-based learning strategy in quantum space, to compute the coefficients of the proposed expression. Meanwhile, we compare GO-QSSA with three swarm intelligence algorithms: artificial bee colony algorithm (ABC), particle swarm optimization algorithm (PSO), and salp swarm algorithm (SSA). The comparing simulation results reveal that GO-QSSA owns faster convergence speed than PSO, ABC, and SSA. Moreover, GO-QSSA is capable of computing more accurately than traditional algorithms. In addition, the simulation results show that compared with existing curve-fitting-based methods, the proposed expression decreases the fitting error by roughly one order of magnitude in most cases and even higher in some cases. Our approximation is proved to be simple and efficient.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

An Efficient Approximation for Nakagami-mQuantile Function Based on Generalized Opposition-Based Quantum Salp Swarm Algorithm

Gao

Hou

Zhang

et al. 2019

Mathematical Problems in Engineering

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show abstract

“…The performance comparison has been done with three sate of art algorithms i.e. SVM classifier (without any optimization technique), Firefly (SVM with Firefly optimization [14]) [1], ACO-PSO-GA (SVM with hybrid ACO-PSO-GA optimization) [9] using sensitivity, specificity, and classification accuracy as the parameters. The analysis parameters i.e.…”

Section: Resultsmentioning

confidence: 99%

Implementation of Hybrid ACO-PSO-GA-DE Algorithm for Mammogram Classification

Bala¹,

Priti²

2019

IJRTE

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Breast Cancer is one of the fastest growing cancer that causes women to death in the world. The early detection of breast cancer improves the chances of its cure. The malignant tumor that is the sign of breast cancer can be detected by mammography. This paper develops a technique to classify the mammogram images as normal, benign or malignant. This paper applies HAPGD (Hybrid ACO (Ant Colony Optimization), PSO (Particle Swarm Optimization), GA (Genetic Algorithm), and DE (Differential Evolution)) classification algorithm to texture features extracted from the mammogram image. The analysis has been done on the DDSM and MIAS dataset by using classification accuracy, specificity, and sensitivity as the parameter with three state of art algorithms i.e. SVM classifier (without any optimization technique), Firefly (SVM with Firefly optimization), ACO-PSO-GA (SVM with hybrid ACO-PSO-GA optimization). The improvement in the performance measures against three state of art techniques shows the significance of the algorithm.

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“…• Multiobjective FA: The standard FA was for single objective optimization and Yang extended the standard FA to multiobjective firefly algorithm (MOFA) for design optimization [77]. In addition, Eswari and Nickolas developed a modified multiobjective FA for task scheduling [13], while Wang et al developed a hybrid multiobjective FA for big data optimization [72], and Zhao et al developed a decomposition-based multiobjective FA for RFID network planning with uncertainty [88].…”

Section: Variants Of Famentioning

confidence: 99%