Quantum-inspired firefly algorithm with particle swarm optimization for discrete optimization problems

Zouache, Djaafar; Nouioua, Farid; Moussaoui, Abdelouahab

doi:10.1007/s00500-015-1681-x

Cited by 83 publications

(36 citation statements)

References 47 publications

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“…When r > sin 2 (θ x,y ), it sets to 1 to select the corresponding conditional feature; otherwise, the value is 0 and reject the corresponding conditional feature. Therefore, due to the superposition state of the qubits, a quantum superposition solution contains many binary solutions [37]. However, each qubit determines the probability of selecting or rejecting the corresponding feature.…”

Section: Quantum Measurement In the Proposed Algorithmmentioning

confidence: 99%

Feature Selection of Grey Wolf Optimizer Based on Quantum Computing and Uncertain Symmetry Rough Set

et al. 2019

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Considering the crucial influence of feature selection on data classification accuracy, a grey wolf optimizer based on quantum computing and uncertain symmetry rough set (QCGWORS) was proposed. QCGWORS was to apply a parallel of three theories to feature selection, and each of them owned the unique advantages of optimizing feature selection algorithm. Quantum computing had a good balance ability when exploring feature sets between global and local searches. Grey wolf optimizer could effectively explore all possible feature subsets, and uncertain symmetry rough set theory could accurately evaluate the correlation of potential feature subsets. QCGWORS intelligent algorithm could minimize the number of features while maximizing classification performance. In the experimental stage, k nearest neighbors (KNN) classifier and random forest (RF) classifier guided the machine learning process of the proposed algorithm, and 13 datasets were compared for testing experiments. Experimental results showed that compared with other feature selection methods, QCGWORS improved the classification accuracy on 12 datasets, among which the best accuracy was increased by 20.91%. In attribute reduction, each dataset had a benefit of the reduction effect of the minimum feature number.

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Section: Quantum Measurement In the Proposed Algorithmmentioning

confidence: 99%

Feature Selection of Grey Wolf Optimizer Based on Quantum Computing and Uncertain Symmetry Rough Set

et al. 2019

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“…Firefly algorithm mimics the behavior of fireflies which is based on flashing and attraction properties of fireflies. Zouache et al [2015] proposed a new hybrid algorithm that combines firefly algorithm and particle swarm optimization and use the basic concepts of quantum computing to ensure a better solution diversity. The proposed algorithm has been tested on 0-1 knapsack problem and multidimensional knapsack problem.…”

Section: Related Workmentioning

confidence: 99%

Nature-inspired optimization algorithms in knapsack problem: A review

Basheer¹,

Algamal²

2019

IQJOSS

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Meta-heuristic algorithms have become an arising field of research in recent years. Some of these algorithms have proved to be efficient in solving combinatorial optimization problems, particularly knapsack problem. In this paper, four meta-heuristic algorithms are presented particle swarm optimization, firefly algorithm, flower pollination algorithm and monarch butterfly optimization in solving knapsack problem as example of NP-hard combinational optimization problems. Based on twenty 0-1 knapsack problem instances, the computational results demonstrated that the binary flower pollination algorithm has the ability to find the best solutions in reasonable time.

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“…The firefly algorithm (FA) was firstly proposed by Yang et al (2008Yang et al ( , 2011Yang et al ( , 2012 [16,17,18], based on which, some further works on FA have been performed by a few researchers. For its characteristics of few input parameters, easy to understand, and implement, it has been applied to various [21] proposed a quantum-inspired firefly algorithm with particle swarm optimisation, which adapted the firefly approach to solving discrete optimisation problems. Besides, some works on a few nature-inspired meta-heuristics and applications have been carried out, such as: Water wave optimisation [23], population classification in fire evacuation [24], rapid learning algorithm for vehicle classification [26], multi-objective optimisation for spatial-temporal efficiency in a heterogeneous cloud environment [27], multiobjective artificial wolf-pack algorithm [28], etc.…”

Section: Introductionmentioning

confidence: 99%

Intelligent swarm firefly algorithm for the prediction of China's national electricity consumption

et al. 2019

IJBIC

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Quantum-inspired firefly algorithm with particle swarm optimization for discrete optimization problems

Cited by 83 publications

References 47 publications

Feature Selection of Grey Wolf Optimizer Based on Quantum Computing and Uncertain Symmetry Rough Set

Feature Selection of Grey Wolf Optimizer Based on Quantum Computing and Uncertain Symmetry Rough Set

Nature-inspired optimization algorithms in knapsack problem: A review

Intelligent swarm firefly algorithm for the prediction of China's national electricity consumption

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