Image steganalysis using improved particle swarm optimization based feature selection

Adeli, Ali; Broumandnia, Ali

doi:10.1007/s10489-017-0989-x

Cited by 56 publications

(30 citation statements)

References 13 publications

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“…To assess the performance of the proposed ICQPSO algorithm, six benchmark functions are examined, and statistical results are compared with PSO [13], SINPSO [17], APSO [18], QPSO [21], and HCQPSO [31,32]. QPSO and PSO are the basic traditional algorithms.…”

Section: Performance Tests Of Icqpso Algorithmmentioning

confidence: 99%

“…It is an effective way to improve the FNN performance by replacing the traditional learning algorithm by metaheuristic algorithms such as the particle swarm optimization (PSO) algorithm [13][14][15]. However, when the considered problem is a complex high-dimensional problem, PSO algorithm has the disadvantage of premature convergence [16][17][18][19]. After studying the results of particle convergence behavior, Sun et al [20] proposed a novel metaheuristic algorithm called quantum-behaved particle swarm optimization (QPSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

Peng

Lei

Yang

et al. 2020

Mathematical Problems in Engineering

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Traditional fuzzy neural network has certain drawbacks such as long computation time, slow convergence rate, and premature convergence. To overcome these disadvantages, an improved quantum-behaved particle swarm optimization algorithm is proposed as the learning algorithm. In this algorithm, a new chaotic search is introduced, and benchmark function experiments prove it outperforms the other five existing algorithms. Finally, the proposed algorithm is presented as the learning algorithm for Takagi-Sugeno fuzzy neural network to form a new neural network, and it is utilized in the water quality evaluation of Dongjiang Lake of Hunan province. Simulation results demonstrated the effectiveness of the new neural network.

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Section: Performance Tests Of Icqpso Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

Peng

Lei

Yang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…However, BPSO suffers from the premature convergence and slow convergence rate [15][16][17]. Additionally, one of the major drawbacks of BPSO is the setting of the inertia weight [18]. In order to solve the limitations of BPSO, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work.…”

Section: Co-evolution Binary Particle Swarm Optimization With Multiplmentioning

confidence: 99%

A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection

2019

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Feature selection is a task of choosing the best combination of potential features that best describes the target concept during a classification process. However, selecting such relevant features becomes a difficult matter when large number of features are involved. Therefore, this study aims to solve the feature selection problem using binary particle swarm optimization (BPSO). Nevertheless, BPSO has limitations of premature convergence and the setting of inertia weight. Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. The proposed method is validated with ten benchmark datasets from UCI machine learning repository. To examine the effectiveness of proposed method, four recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary gravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are used in a performance comparison. Our results show that CBPSO-MIWS can achieve competitive performance in feature selection, which is appropriate for application in engineering, rehabilitation and clinical areas.

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“…Each of these decisions can be modeled as an optimization problem or, in many cases, a combinatorial optimization problem (COP). Examples of COPs are found in different areas: machine learning [1], transportation [2], facility layout design [3], logistics [4], scheduling problems [2,5], resource allocation [6,7], routing problems [8], robotics applications [9], image analysis [10], engineering design problems [11], fault diagnosis of machinery [12], and manufacturing problems [13], among others. If the problem is large, metaheuristics have been a good approximation to obtain adequate solutions.…”

Section: Introductionmentioning

confidence: 99%

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

et al. 2020

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This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.

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Image steganalysis using improved particle swarm optimization based feature selection

Cited by 56 publications

References 13 publications

Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

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