An Optimized Level Set Method Based on QPSO and Fuzzy Clustering

Abstract

A new fuzzy level set method (FLSM) based on the global search capability of quantum particle swarm optimization (QPSO) is proposed to improve the stability and precision of image segmentation, and reduce the sensitivity of initialization. The new combination of QPSO-FLSM algorithm iteratively optimizes initial contours using the QPSO method and fuzzy c-means clustering, and then utilizes level set method (LSM) to segment images. The new algorithm exploits the global search capability of QPSO to obtain a stable cluster center and a pre-segmentation contour closer to the region of interest during the iteration. In the implementation of the new method in segmenting liver tumors, brain tissues, and lightning images, the fitness function of the objective function of QPSO-FLSM algorithm is optimized by 10% in comparison to the original FLSM algorithm. The achieved initial contours from the QPSO-FLSM algorithm are also more stable than that from the FLSM. The QPSO-FLSM resulted in improved final image segmentation.

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

“…The simulation results revealed that compared with the artificial neural network (ANN) and classical meta-heuristic optimization algorithm, QPSO exhibited advantages in accuracy, robustness, and rapid convergence to the global optimum. Yang et al [20] used QPSO to solve image segmentation and found that, compared with other optimization algorithms, QPSO improved the stability and accuracy of image segmentation. The economic dispatch problem in power systems is a typical high-dimensional nonlinear problem.…”

“…The equivalent division of the qualitative indicators includes qualitative language descriptions and corresponding quantitative data intervals. 5) [5, 10) [10, 100] R 12 (%) [0, 5) [5, 10) [10, 20) [20, 40) [40, 100] R 13 [0, 3) [3, 5) [5, 10) [10, 20) [20, 100] R 14 (%) [0, 5) [5, 10) [10, 20) [20, 40) [40, 100] R 15 (%) [0, 5) [5, 10) [10, 20) [20, 40) [40, 100] R 21 (%) [0, 1) [1, 3) [3, 5) [5, 10) [10, 100] R 22 (%) [0, 1) [1, 3) [3, 5) [5, 10) [10, 100] R 23 (%) [0, 3) [3, 5) [5, 10) [10, 20) [20, 100] R 24 (%) [0, 3) [3, 5) [5, 10) [10, [90, 95) [85, 90) [80, 85) [0, 80) R 34 (%) [0, 3) [3, 5) [5, 10) [10, 20) [20, 100] R 41 (mm) [0, 30) [30, 50) [50, PSO is a meta-heuristic method that can realize the global optimization of multiextremum functions. The particles in the population search for the global optimum of the function via cooperation and competition and share or exchange the information they obtained in their respective search processes.…”

“…Excluding the total number of particles N and the maximum number of iterations t max in the population, the QPSO has only one parameter, namely the compression-expansion factor α(t). To simplify the algorithm to the greatest extent, the value strategy of α linear reduction is chosen for use [20]:…”

“…According to the type of indicator, the normalized early warning data could be obtained by introducing the data in Table 5 into Equation (19) or (20). The normalized early warning data were then introduced into Equation ( 21) to obtain the correlation analysis results of each index (input variable) and the early warning level (output variable), as shown in Table 6.…”

“…Quantum-behaved particle swarm optimization (QPSO) replaces the displacement updating formula and velocity updating formula in the traditional PSO with the wave function of quantum mechanics [18]. Because the particles in QPSO appear in all dimensions of the solution space in the form of probability, it has a better global retrieval ability than the classical meta-heuristic optimization algorithms from the aspects of the prediction and optimization of turning surface roughness [19], image segmentation optimization [20], and economic scheduling optimization [21].…”