Fitness Function Of Particle Swarm Optimization Research Articles

Evolutionary Computation Methods in Image Watermarking

Watermarking can be described as a desirable alternative solution for copyright protection of digital materials like images, video and audio. This paper introduced a modified watermarking method in Wavelet domain to get better performance. Particle Swarm Optimization (PSO) is used to find the best DWT coefficients in the HL sub - band for embedding the watermark sequence and PSO fitness function can be formed from the watermarked image with the best possible quality, and proved execution time.

Particle Swarm Optimization Based Support Vector Regression for Blind Image Restoration

This paper presents a swarm intelligence based parameter optimization of the support vector machine (SVM) for blind image restoration. In this work, SVM is used to solve a regression problem. Support vector regression (SVR) has been utilized to obtain a true mapping of images from the observed noisy blurred images. The parameters of SVR are optimized through particle swarm optimization (PSO) technique. The restoration error function has been utilized as the fitness function for PSO. The suggested scheme tries to adapt the SVM parameters depending on the type of blur and noise strength and the experimental results validate its effectiveness. The results show that the parameter optimization of the SVR model gives better performance than conventional SVR model as well as other competent schemes for blind image restoration.

Inverse transient radiation analysis in one-dimensional non-homogeneous participating slabs using particle swarm optimization algorithms

Particle Swarm Optimization (PSO) algorithms, including standard PSO, Stochastic PSO, and Multi-Phase PSO, are applied to solve the time-domain inverse transient radiation problems in the present research. Time-resolved transmittance and reflectance signals of four different measuring models serve as the measurement data, which estimate absorption, scattering coefficients, and geometric position within one-dimensional non-homogeneous media by inverse simulation. To check retrieval performances and accuracies of PSO-based approaches, four different inverse transient radiation cases are investigated to deal with one homogeneous layer, two-layer, three-layer, and continuous non-homogenous media. The influences of different searching ranges, swarm sizes, and maximum fly velocities on the fitness function of PSO are discussed. Meanwhile, the effects of measurement errors on the reconstruction accuracy are also investigated. All the results confirm that radiative parameters could be estimated accurately with measurement noise using PSO-based approaches.

A RELEVANCE FEEDBACK APPROACH BASED ON MODIFICATION OF SIMILARITY MEASURE USING PARTICLE SWARM OPTIMIZATION IN A MEDICAL X-RAY IMAGE RETRIEVAL SYSTEM

Relevance feedback (RF) approaches are use to improve the performance of content-based image retrieval (CBIR) systems. In this paper, a RF approach based on modification of similarity measure using particle swarm optimization (PSO) in a medical X-ray image retrieval system is proposed. In this algorithm, using PSO, the significance of each feature in the similarity measure is modified to image retrieval. This modification causes that good features have major effect in relevant image retrieval. The defined fitness function in PSO uses relevant and irrelevant retrieved images with different strategies, simultaneously. The relevant and irrelevant images are used to exhort and penalize similarity measure, respectively. To evaluate, the proposed RF is integrated to a CBIR system based on semantic classification. In this system, using merging scheme in a hierarchical structure, the overlapped classes are merged together and determined search space for each query image. The proposed RF evaluated on a database consisting of 10000 medical X-ray images of 57 classes. The proposed algorithm provides the improvement, effectiveness more than the literature.

Particle Swarm Optimization Algorithm for a Batching Problem in the Process Industry

An improved particle swarm optimization (PSO) algorithm is proposed to solve a typical batching problem in a batch processing plant of the process industry. The batching problem (BP) is to transform the primary requirements for products into sets of batches for each task with the objective of minimizing the total workload. On the basis of some preliminary properties, a novel particle solution representation is designed for the BP. Unlike the ordinary idea of taking an objective function as the fitness function for PSO, the original objective function incorporated with a constraint function is to act as the fitness function of the PSO where the constraint and the objective functions are evaluated successively. Such a fitness function, together with a forward repair mechanism, makes it possible for a faster convergence. Further, for each iterative generation, a local search heuristic is used to improve the global best particle found so far. To verify the performance of the proposed PSO algorithm, the well-k...

A hybrid particle swarm optimization approach with neural network and set pair analysis for transmission network planning

Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.