A Triangle Mesh Standardization Method Based on Particle Swarm Optimization.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang W
PROVIDER: S-EPMC4979957 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:This paper introduces a novel variant of the quantum particle swarm optimization algorithm based on the quantum concept of particle-like solitons as the most common solutions of the quantum nonlinear Schrödinger equation. Soliton adaptation in external potentials is one of their most remarkable features which allows them to be stabilized even without a trapping potential, while the potential must be bounded for quantum particles to be localized. So we consider the motion scenario of the present algorithm based on the corresponding probability density function of quantum solitons. To evaluate the efficiency, we examine the proposed algorithm over a set of known benchmark functions, including a selection of test functions with different modalities and dimensions. Moreover, to achieve a more comprehensive conclusion about the performance, we compare it with the results obtained by particle swarm optimization (PSO), standard quantum-behaved particle swarm optimization QPSO, improved sine cosine Algorithm (ISCA), and JAYA optimization algorithm. The numerical experiments reveal that the proposed algorithm is an effective approach to solving optimization problems that provides promising results in terms of better global search capability, high accuracy, and faster convergence rate.
| S-EPMC9385677 | biostudies-literature
Project description:PurposeAlthough classical techniques for image segmentation may work well for some images, they may perform poorly or not work at all for others. It often depends on the properties of the particular image segmentation task under study. The reliable segmentation of brain tumors in medical images represents a particularly challenging and essential task. For example, some brain tumors may exhibit complex so-called "bottle-neck" shapes which are essentially circles with long indistinct tapering tails, known as a "dual tail." Such challenging conditions may not be readily segmented, particularly in the extended tail region or around the so-called "bottle-neck" area. In those cases, existing image segmentation techniques often fail to work well.MethodsExisting research on image segmentation using wormhole and entangle theory is first analyzed. Next, a random positioning search method that uses a quantum-behaved particle swarm optimization (QPSO) approach is improved by using a hyperbolic wormhole path measure for seeding and linking particles. Finally, our novel quantum and wormhole-behaved particle swarm optimization (QWPSO) is proposed.ResultsExperimental results show that our QWPSO algorithm can better cluster complex "dual tail" regions into groupings with greater adaptability than conventional QPSO. Experimental work also improves operational efficiency and segmentation accuracy compared with current competing reference methods.ConclusionOur QWPSO method appears extremely promising for isolating smeared/indistinct regions of complex shape typical of medical image segmentation tasks. The technique is especially advantageous for segmentation in the so-called "bottle-neck" and "dual tail"-shaped regions appearing in brain tumor images.
| S-EPMC9127532 | biostudies-literature
Project description:In recent years, comprehensive learning particle swarm optimization (CLPSO) has attracted the attention of many scholars for using in solving multimodal problems, as it is excellent in preserving the particles' diversity and thus preventing premature convergence. However, CLPSO exhibits low solution accuracy. Aiming to address this issue, we proposed a novel algorithm called LILPSO. First, this algorithm introduced a Lagrange interpolation method to perform a local search for the global best point (gbest). Second, to gain a better exemplar, one gbest, another two particle's historical best points (pbest) are chosen to perform Lagrange interpolation, then to gain a new exemplar, which replaces the CLPSO's comparison method. The numerical experiments conducted on various functions demonstrate the superiority of this algorithm, and the two methods are proven to be efficient for accelerating the convergence without leading the particle to premature convergence.
| S-EPMC4849747 | biostudies-literature
Project description:BackgroundIdentifying approximately repeated patterns, or motifs, in DNA sequences from a set of co-regulated genes is an important step towards deciphering the complex gene regulatory networks and understanding gene functions.ResultsIn this work, we develop a novel motif finding algorithm (PSO+) using a population-based stochastic optimization technique called Particle Swarm Optimization (PSO), which has been shown to be effective in optimizing difficult multidimensional problems in continuous domains. We propose a modification of the standard PSO algorithm to handle discrete values, such as characters in DNA sequences. The algorithm provides several features. First, we use both consensus and position-specific weight matrix representations in our algorithm, taking advantage of the efficiency of the former and the accuracy of the latter. Furthermore, many real motifs contain gaps, but the existing methods usually ignore them or assume a user know their exact locations and lengths, which is usually impractical for real applications. In comparison, our method models gaps explicitly, and provides an easy solution to find gapped motifs without any detailed knowledge of gaps. Our method allows the presence of input sequences containing zero or multiple binding sites.ConclusionExperimental results on synthetic challenge problems as well as real biological sequences show that our method is both more efficient and more accurate than several existing algorithms, especially when gaps are present in the motifs.
| S-EPMC3022572 | biostudies-literature
Project description:In this paper, a battery charging model is developed for solar PV system applications. As a means of photovoltaic power controlling system, buck-boost converter with a Maximum Power Point Tracking (MPPT) mechanism is developed in this paper for maximum efficiency. This paper proposed a novel combined technique of hybrid Particle Swarm Optimisation (PSO) and Salp Swarm Optimization (SSO) models to perform Maximum Power Point Tracking mechanisms and obtain a higher efficiency for battery charging. In order to retrieve the maximum power from the PV array, the Maximum Power Point Tracking mechanism is observed which reaches the maximum efficiency and the maximum power is fed through the buck-boost converter into the load. The buck-boost converter steps up the voltage to essential magnitude. The energy drawn from the PV array is used for the battery charging by means of an isolated buck converter since the buck-boost converter is not directly connected to the battery. The Fractional Order Proportional Integral Derivative (FOPID) controller handles the isolated buck converter and battery to enhance the efficiency obtained through the Maximum Power Point Tracking mechanism. The simulation results show higher steady efficiency by using the hybrid PSOSSO algorithm in all stages. The battery is charged without losing the efficiency obtained from the hybrid PSOSSO algorithm-based Maximum Power Point Tracking mechanism. The higher efficiency was obtained as 99.99% at Standard Test Conditions (STC) and 99.52% at PV partial shading conditions (PSCs) by using the new hybrid algorithm.
| S-EPMC8854737 | biostudies-literature
Project description:Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.
| S-EPMC4365407 | biostudies-other
Project description:The improvement and development of blood-contacting devices, such as mechanical circulatory support systems, is a life saving endeavor. These devices must be designed in such a way that they ensure the highest hemocompatibility. Therefore, in-silico trials (flow simulations) offer a quick and cost-effective way to analyze and optimize the hemocompatibility and performance of medical devices. In that regard, the prediction of blood trauma, such as hemolysis, is the key element to ensure the hemocompatibility of a device. But, despite decades of research related to numerical hemolysis models, their accuracy and reliability leaves much to be desired. This study proposes a novel optimization path, which is capable of improving existing models and aid in the development of future hemolysis models. First, flow simulations of three, turbulent blood flow test cases (capillary tube, FDA nozzle, FDA pump) were performed and hemolysis was numerically predicted by the widely-applied stress-based hemolysis models. Afterward, a multiple-objective particles swarm optimization (MOPSO) was performed to tie the physiological stresses of the simulated flow field to the measured hemolysis using an equivalent of over one million numerically determined hemolysis predictions. The results show that our optimization is capable of improving upon existing hemolysis models. However, it also unveils some deficiencies and limits of hemolysis prediction with stress-based models, which will need to be addressed in order to improve its reliability.
| S-EPMC10097800 | biostudies-literature
Project description:The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances.
| S-EPMC7063896 | biostudies-literature
Project description:An operon is a fundamental unit of transcription and contains specific functional genes for the construction and regulation of networks at the entire genome level. The correct prediction of operons is vital for understanding gene regulations and functions in newly sequenced genomes. As experimental methods for operon detection tend to be nontrivial and time consuming, various methods for operon prediction have been proposed in the literature. In this study, a binary particle swarm optimization is used for operon prediction in bacterial genomes. The intergenic distance, participation in the same metabolic pathway, the cluster of orthologous groups, the gene length ratio and the operon length are used to design a fitness function. We trained the proper values on the Escherichia coli genome, and used the above five properties to implement feature selection. Finally, our study used the intergenic distance, metabolic pathway and the gene length ratio property to predict operons. Experimental results show that the prediction accuracy of this method reached 92.1%, 93.3% and 95.9% on the Bacillus subtilis genome, the Pseudomonas aeruginosa PA01 genome and the Staphylococcus aureus genome, respectively. This method has enabled us to predict operons with high accuracy for these three genomes, for which only limited data on the properties of the operon structure exists.
| S-EPMC2896535 | biostudies-literature
Project description:The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.
| S-EPMC4433345 | biostudies-literature