Ship inner shell optimization based on the improved particle swarm optimization algorithm

Guan, Guan; Yang, Qing; Gu, Wenwen; Jiang, Wen-ying; Lin, Yan

doi:10.1016/j.advengsoft.2018.06.012

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…The propagation rate of inner link edge is defined by φ(e i ), the propagation rate of outer link edge is defined by ϕ(e o ), and the propagation rate of entity identification edge is defined by ψ(e r ). The vertex credibility after n times credibility value propagation can be used to calculate the vertex credibility value after n + 1 times propagation, and the corresponding equation is listed as follows [13]:…”

Section: Framework Of Web Big Datamentioning

confidence: 99%

Credibility Evaluation of Web Big Data Information Based on Particle Swarm Optimization

Zhao

2022

JWE

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In order to improve the credibility evaluation effectiveness of web big data information, the improved particle swarm optimization is established. Firstly, framework of web big data is designed to include web big data source, data storage, data processing and data analysis. The global credibility calculation formula of whole web is established. Secondly, the improved particle swarm algorithm is constructed through updating weight and training factor, introducing cross and mutation operations into the algorithm, and improving population diversification based on mountain climbing algorithm. Finally, ten sample industries and one hundred sample stocks are selected to carry out experiment analysis, and the results show that the proposed algorithm can effectively distinguish trusted and untrustworthy records. And the proposed algorithm of calculating credibility of web big data has high reasonableness.

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Section: Framework Of Web Big Datamentioning

confidence: 99%

Credibility Evaluation of Web Big Data Information Based on Particle Swarm Optimization

Zhao

2022

JWE

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“…e parameters of PSO algorithm directly affect the performance of PSO algorithm, so we must consider it again when choosing parameters, in order to improve the convergence speed of the algorithm and find the global optimal solution. Inertia weight can balance the local and global distribution of particle swarm optimization search ability [11]. In order to ensure the global search ability at the beginning of the iteration and the local search ability at the later stage of the iteration, the inertia weight should be gradually reduced with the increase of iteration times.…”

Section: Influence Of Algorithm Parameter Settingmentioning

confidence: 99%

Particle Swarm Optimization Algorithm in Numerical Simulation of Saturated Rock Slope Slip

Liu

Wang

Zhong

2021

Mathematical Problems in Engineering

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With the continuous popularization and development of highway traffic in mountainous areas, the number of rock slopes is also increasing. In order to improve the stability of rock slope and reduce the harm caused by slope slip, this paper carries out numerical simulation of rock slope sliding based on particle swarm optimization algorithm. Firstly, this paper combines the differential evolution algorithm and simplex method to improve the global and local search ability of particle swarm optimization (PSO) algorithm and analyzes the performance of the algorithm. ABAQUS software is used to simulate rock slope sliding, the finite element method is used to analyze the stability of rock slope, and LS-DYNA program is used to simulate rockfall impact rock slope. During the numerical simulation, the improved algorithm is used to analyze all the data. Experimental data show that the improved PSO algorithm converges after nearly 100 iterations and the convergence speed and optimization accuracy are high. In the numerical simulation, the average failure probability of the left and right sides of the main section at the top, middle, and foot of the slope is 0.0820 and 0.0723, 0.0772 and 0.0492, and 0.0837 and 0.0677, respectively, indicating that the overall instability probability of the left side of the rock slope is higher than that of the right side. The rock slope with the same direction through joint is mainly affected by the joint at the toe of the slope, the rock slope with reverse through joint is mainly affected by the joint in the slope, and the sliding occurs from the middle to both ends. In addition, with the increase of the size and height of rockfall, the total energy of rock slope is also increasing, and the possibility and degree of rock slope sliding are higher. This shows that the improved particle swarm optimization algorithm can effectively analyze some factors affecting slope slip in numerical simulation of saturated rock slope slip.

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“…PS-CTPSO [61] based on the predatory search strategy was proposed to do with web service combinatorial optimization, which is an NP problem, and it improves overall ergodicity. To improve the changeability of ship inner shell, IPSO [62] was proposed for a 50,000 DWT product oil tanker. MBPSO [63] was proposed for sensor management of LEO constellation to the problem of utilizing a low Earth orbit (LEO) infrared constellation in order to track the midcourse ballistic missile.…”

Section: Introductionmentioning

confidence: 99%

A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization

2018

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In order to overcome the several shortcomings of Particle Swarm Optimization (PSO) e.g., premature convergence, low accuracy and poor global searching ability, a novel Simple Particle Swarm Optimization based on Random weight and Confidence term (SPSORC) is proposed in this paper. The original two improvements of the algorithm are called Simple Particle Swarm Optimization (SPSO) and Simple Particle Swarm Optimization with Confidence term (SPSOC), respectively. The former has the characteristics of more simple structure and faster convergence speed, and the latter increases particle diversity. SPSORC takes into account the advantages of both and enhances exploitation capability of algorithm. Twenty-two benchmark functions and four state-of-the-art improvement strategies are introduced so as to facilitate more fair comparison. In addition, a t-test is used to analyze the differences in large amounts of data. The stability and the search efficiency of algorithms are evaluated by comparing the success rates and the average iteration times obtained from 50-dimensional benchmark functions. The results show that the SPSO and its improved algorithms perform well comparing with several kinds of improved PSO algorithms according to both search time and computing accuracy. SPSORC, in particular, is more competent for the optimization of complex problems. In all, it has more desirable convergence, stronger stability and higher accuracy.

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Ship inner shell optimization based on the improved particle swarm optimization algorithm

Cited by 11 publications

References 10 publications

Credibility Evaluation of Web Big Data Information Based on Particle Swarm Optimization

Credibility Evaluation of Web Big Data Information Based on Particle Swarm Optimization

Particle Swarm Optimization Algorithm in Numerical Simulation of Saturated Rock Slope Slip

A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization

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