Improved magnetic charged system search optimization algorithm with application to satellite formation flying

D’Ambrosio, Andrea; Spiller, Dario; Curti, Fabio

doi:10.1016/j.engappai.2020.103473

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…It can comprehensively process incomplete data through induction, learning, and mining to build a relatively clear and concise data system to support subsequent composition analysis. Based on the above analysis, this paper uses rough set theory to collect mass physical health data [ 12 ], supplement incomplete data and indicators, and finally get a clearer data system. The specific data processing flow is shown in Figure 1 .…”

Section: The Algorithm Description Based Online Sequential Extreme Le...mentioning

confidence: 99%

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

Cheng

Liu

2022

Computational Intelligence and Neuroscience

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The rapid development of social economy not only increases people’s living pressure but also reduces people’s health. Looking for a healthy development prediction model has become a domestic concern. Based on the analysis of the influencing factors of health development, this paper looks for a model to predict the development of public health, so as to improve the accuracy of health development prediction. In this paper, the linear sequential extreme learning machine algorithm can be used to evaluate the health status of a large number of data, analyze the differences of each evaluation index, and construct the analysis model of health status. Therefore, this paper introduces rough set theory into linear sequential extreme learning machine algorithm. Rough set can analyze the double analysis of evaluation scheme, predict the health development of different individuals, and improve the evaluation accuracy of mass health evaluation. The simulation results show that the improved line sequential extreme learning machine algorithm can accurately analyze the mass health and meet the needs of different individuals’ health evaluation.

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Section: The Algorithm Description Based Online Sequential Extreme Le...mentioning

confidence: 99%

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

Cheng

Liu

2022

Computational Intelligence and Neuroscience

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“…The studies of improved PSO are mainly related to modified PSO algorithms and hybrid PSO algorithms based on meta-heuristic approaches [37]. Modified PSO algorithms are based on the updated model of PSO and adopted some strategies and methods in the search process of particles, such as flight mechanisms of particles including levy flight [38][39][40], learning strategies for particles including cirssoss learning [41], cognitive learning [42] and comprehensive learning [40]; population topology including stochastic topology [7] and dynamic topology [43]; and optimization strategies including random walk strategy [44], chaos strategy [45] and synergistic strategy [46]; search strategies including local search [47,48] and charged system search [49,50]. Hybrid PSO algorithms are combined with some traditional and evolutionary optimization methods in order to utilized the advantages of both methods and improve the global search ability of PSO, such as simulated annealing (SA) [51], tabu search (TS) [52], BBO [53], artificial bee colony (ABC) [54], genetic algorithm (GA) [55] and differential evolution (DE) [56].…”

Section: Related Workmentioning

confidence: 99%

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

Wang

Liu

et al. 2022

Mathematics

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An extended membrane system with a dynamic nested membrane structure, which is integrated with the evolution-communication mechanism of a cell-like P system with evolutional symport/antiport rules and active membranes (ECP), and the evolutionary mechanisms of particle swarm optimization (PSO) and improved PSO inspired by starling flock behavior (SPSO), named DSPSO-ECP, is designed and developed to try to break application restrictions of P systems in this paper. The purpose of DSPSO-ECP is to enhance the performance of extended membrane system in solving optimization problems. In the proposed DSPSO-ECP, the updated model of velocity and position of standard PSO, as basic evolution rules, are adopted to evolve objects in elementary membranes. The modified updated model of the velocity of improved SPSO is used as local evolution rules to evolve objects in sub-membranes. A group of sub-membranes for elementary membranes are specially designed to avoid prematurity through membrane creation and dissolution rules with promoter/inhibitor. The exchange and sharing of information between different membranes are achieved by communication rules for objects based on evolutional symport rules of ECP. At last, computational results, which are made on numerical benchmark functions and classic test images, are discussed and analyzed to validate the efficiency of the proposed DSPSO-ECP.

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A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism

Zhao

Zhou

et al. 2022

Expert Systems with Applications

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Improved magnetic charged system search optimization algorithm with application to satellite formation flying

Cited by 5 publications

References 45 publications

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism

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