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. 2017 Dec 18;7(1):17733.
doi: 10.1038/s41598-017-17945-3.

An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection

Xingmei Wang  1 , Wenqian Hao  2 , Qiming Li  3
Affiliations

An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection

Xingmei Wang et al. Sci Rep. .

Abstract

This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Schematic diagram of CA.
Figure 2
Figure 2
Position distribution of particles in IQPSO and QPSO. (a) Position distribution of particles on Sphere function. (b) Position distribution of particles on Griewank function.
Figure 3
Figure 3
The optimization results in IQPSO and QPSO. (a) The optimization results of Sphere function. (b) The optimization results of Griewank function.
Figure 4
Figure 4
The optimization results in ACA-IQPSO and CA-IQPSO. (a) The optimization results of Sphere function. (b) The optimization results of Griewank function.
Figure 5
Figure 5
Detection results of original sonar image (image size: 277 × 325). (a) Original sonar image. (b) Smoothed image. (c) ACA-IQPSO. (d) CA-IQPSO. (e) CPSO. (f) IQPSO. (g) QPSO. (h) PSO.
Figure 6
Figure 6
Detection results of original sonar image (image size: 203 × 257). (a) Original sonar image. (b) Smoothed image. (c) ACA-IQPSO. (d) CA-IQPSO. (e) CPSO. (f) IQPSO. (g) QPSO. (h) PSO.
Figure 7
Figure 7
Detection results of original sonar image (image size: 259 × 368). (a) Original sonar image. (b) Smoothed image. (c) ACA-IQPSO. (d) CA-IQPSO. (e) CPSO. (f) IQPSO. (g) QPSO. (h) PSO.
Figure 8
Figure 8
Detection results of original sonar image (image size: 173 × 167). (a) Original sonar image. (b) Smoothed image. (c) ACA-IQPSO. (d) CA-IQPSO. (e) CPSO. (f) IQPSO. (g) QPSO. (h) PSO.
Figure 9
Figure 9
Variation of the fitness values in each iteration. (a) Variation of fitness values of Fig. 5. (b) Variation of fitness values of Fig. 6. (c) Variation of fitness values of Fig. 7. (d) Variation of fitness values of Fig. 8.
Figure 10
Figure 10
Detection results of original sonar image (image size: 130 × 201). (a) Original sonar image. (b) Smoothed image. (c) Detection result of ACA-IQPSO.
Figure 11
Figure 11
Detection results of original sonar image (image size: 393 × 218). (a) Original sonar image. (b) Smoothed image. (c) Detection result of ACA-IQPSO.
Figure 12
Figure 12
Detection results of original sonar image (image size: 197 × 211). (a) Original sonar image. (b) Smoothed image. (c) Detection result of ACA-IQPSO.
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