Trajectory optimization of the exploration of asteroids using swarm intelligent algorithms

Zhu, Kaijian; Li, Junfeng; Baoyin, Hexi

doi:10.1016/s1007-0214(10)70022-5

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…(5) Designing SI and EAs Over the past years, SI and EAs have been successfully used to handle various complex optimization problems [148][149][150][151][152] . According to the analysis and discussion, they have excellent performance in addressing distributed scheduling problems in manufacturing systems, particularly those with complicated constraints and large solution spaces.…”

Section: Conclusion and Further Directionmentioning

confidence: 99%

Distributed scheduling problems in intelligent manufacturing systems

Hou

Wang

et al. 2021

Tsinghua Sci. Technol.

137

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Currently, manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization. Hence, they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations.Nowadays, distributed manufacturing systems have been widely adopted in industrial production processes. In recent years, many studies have been done on the modeling and optimization of distributed scheduling problems.This work provides a literature review on distributed scheduling problems in intelligent manufacturing systems. By summarizing and evaluating existing studies on distributed scheduling problems, we analyze the achievements and current research status in this field and discuss ongoing studies. Insights regarding prior works are discussed to uncover future research directions, particularly swarm intelligence and evolutionary algorithms, which are used for managing distributed scheduling problems in manufacturing systems. This work focuses on journal papers discovered using Google Scholar. After reviewing the papers, in this work, we discuss the research trends of distributed scheduling problems and point out some directions for future studies.

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Section: Conclusion and Further Directionmentioning

confidence: 99%

Distributed scheduling problems in intelligent manufacturing systems

Hou

Wang

et al. 2021

Tsinghua Sci. Technol.

137

View full text Add to dashboard Cite

show abstract

“…(1) Initial particle The choice of initial number of particles and the criterion of convergence vary among different applications [6,7,[10][11][12] . However, our algorithm differed at several points from these PSO algorithms.…”

Section: Initial Stepmentioning

confidence: 99%

“…Second, if after a long search the swarm became very crowded (judged by its variance) but was still unable to achieve the minimum performance, this was considered a compromise result under poor conditions. Finally, if the swarm had still not converged after 800 rounds (i.e., 10 times of the number of the particles [11] ), the search was stopped and the most recent best solution was accepted as the result. This was introduced to stop the system running for an extended period under poor conditions.…”

Section: Convergence Criterionmentioning

confidence: 99%

Optimization of the occlusion strategy in visual tracking

Wen

Zhang

2016

Tinshhua Sci. Technol.

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Interference and anti-interference are two opposite and important issues in visual tracking. Occlusion interference can disguise the features of a target and can also be used as an effective benchmark to determine whether a tracking algorithm is reliable. In this paper, we proposed an inner Particle Swarm Optimization (PSO) algorithm to locate the optimal occlusion strategy under different tracking conditions and to identify the most effective occlusion positions and direction of movement to allow a target to evade tracking. This algorithm improved the standard PSO process in three ways. First, it introduced a death process, which greatly reduced the time cost of optimization. Second, it used statistical data to determine the fitness value of the particles so that the fitness more accurately described the tracking. Third, the algorithm could avoid being trapped in local optima, as the fitness changes with time. Experimental results showed that this algorithm was able to identify a global optimal occlusion strategy that can disturb the tracking machine with 86.8% probability over more than 10 000 tracking processes. In addition, it reduced the time cost by approximately 80%, compared with conventional PSO algorithms.

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“…Clustering methods aim to partition data into appropriate clusters based on certain objectives or rules [6,7] , such as minimization of the within-classesvariance or maximization of the between-classesvariance. Since clustering can be regarded as an optimization problem, Evolutionary Algorithms (EAs) as powerful optimizers [8,9] , are often applied, e.g., Particle Swarm Optimization (PSO) algorithm [10,11] and Genetic Algorithm (GA) [12,13] .…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Multi-Tasking Optimization for High-Efficiency Time Series Data Clustering

Wang,

Li,

Shen

et al. 2024

Tsinghua Sci. Technol.

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Time series clustering is a challenging problem due to the large-volume, high-dimensional, and warping characteristics of time series data. Traditional clustering methods often use a single criterion or distance measure, which may not capture all the features of the data. This paper proposes a novel method for time series clustering based on evolutionary multi-tasking optimization, termed i-MFEA, which uses an improved multifactorial evolutionary algorithm to optimize multiple clustering tasks simultaneously, each with a different validity index or distance measure.Therefore, i-MFEA can produce diverse and robust clustering solutions that satisfy various preferences of decisionmakers. Experiments on two artificial datasets show that i-MFEA outperforms single-objective evolutionary algorithms and traditional clustering methods in terms of convergence speed and clustering quality. The paper also discusses how i-MFEA can address two long-standing issues in time series clustering: the choice of appropriate similarity measure and the number of clusters.

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Trajectory optimization of the exploration of asteroids using swarm intelligent algorithms

Cited by 8 publications

References 14 publications

Distributed scheduling problems in intelligent manufacturing systems

Distributed scheduling problems in intelligent manufacturing systems

Optimization of the occlusion strategy in visual tracking

Evolutionary Multi-Tasking Optimization for High-Efficiency Time Series Data Clustering

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