A Modified Multi-Objective Particle Swarm Optimization Based on Levy Flight and Double-Archive Mechanism

Guan, Tianhua; Han, Fei; Han, Henry

doi:10.1109/access.2019.2960472

Cited by 24 publications

(11 citation statements)

References 31 publications

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“…2RHyRec [12] is a ranking oriented hybrid approach that combines collaborative filtering with potential factors. Model-based methods have used machine learning methods [34][35][36] such as clustering model [25][37-39], neural networks [17][40-42] and latent semantic model [1][37] [43][44][45][46].…”

Section: Related Workmentioning

confidence: 99%

QoS prediction for smart service management and recommendation based on the location of mobile users

et al. 2022

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Section: Related Workmentioning

confidence: 99%

QoS prediction for smart service management and recommendation based on the location of mobile users

et al. 2022

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“…The random distribution is altered by changing the value by using a different value for ß [30]. The distribution is frequently expressed as equation (6), where ß parameter is an index in the range (0,2] [35].…”

Section: Particle Swarm Optimization Levy Flight (Pso-lf)mentioning

confidence: 99%

A particle swarm optimization levy flight algorithm for imputation of missing creatinine dataset

Ismail

Aziz

Ralib

et al. 2021

Int. J. Adv. Intell. Informatics

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Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient Acute Kidney Injury (AKI) and those at high risk of developing AKI could be identified. This paper proposes an improved mechanism to machine learning imputation algorithms by introducing the Particle Swarm Levy Flight algorithm. We improve the algorithms by modifying the Particle Swarm Optimization Algorithm (PSO), by enhancing the algorithm with levy flight (PSOLF). The creatinine dataset that we collected, including AKI diagnosis and staging, mortality at hospital discharge, and renal recovery, are tested and compared with other machine learning algorithms such as Genetic Algorithm and traditional PSO. The proposed algorithms' performances are validated with a statistical significance test. The results show that SVMPSOLF has better performance than the other method. This research could be useful as an important tool of prognostic capabilities for determining which patients are likely to suffer from AKI, potentially allowing clinicians to intervene before kidney damage manifests.

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“…The performance of the algorithm varies greatly according to the index. This type of algorithm is suitable for dealing with low-dimensional problems [33], and the computing performance drops significantly as the dimensionality increases.…”

Section: Introductionmentioning

confidence: 99%

A Competitive Particle Swarm Algorithm Based on Vector Angles for Multi-Objective Optimization

2021

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Recently, the particle swarm algorithm (PSO) has demonstrated its effectiveness in solving multi-objective optimization problems. However, the performance of most existing multi-objective particle swarm algorithms depends largely on the global or individual best particles. Moreover, due to the rapid convergence of PSO in single objective optimization problems, PSO is prone to poorly distributed indicators when dealing with multi-objective optimization problems. To solve the above problems, we propose a multi-objective competitive particle swarm algorithm based on vector angles (VaCSO). Firstly, in order to remove the influence of global best particles or individual best particles on the algorithm, the competition mechanism is used. Secondly, in order to increase the diversity of solutions while maintaining the convergence of the algorithm, the population is clustered into two populations. Population 1 mainly considers the convergence of the solution in the offspring generation strategy. As a supplement, population 2 adds a new offspring generation strategy to maintain the distribution of the solution, and we innovatively proposed a three-particle competition to improve the distribution and diversity of particle swarms. Finally, based on vector angle information, we consider auxiliary learning to optimize the population gap, so as to improve the distribution of the algorithm. We have established two sets of comparative experiments to test the performance of VaCSO. We compared VaCSO with the currently popular multi-objective particle swarm optimizers and multi-objective evolutionary algorithms. Experimental results show that VaCSO has an excellent performance in convergence and distribution, and has a significant effect in optimizing quality.INDEX TERMS multi-objective optimization, competition particle swarm, competition mechanism, threeparticle competition, vector angle information, auxiliary learning.

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A Modified Multi-Objective Particle Swarm Optimization Based on Levy Flight and Double-Archive Mechanism

Cited by 24 publications

References 31 publications

QoS prediction for smart service management and recommendation based on the location of mobile users

QoS prediction for smart service management and recommendation based on the location of mobile users

A particle swarm optimization levy flight algorithm for imputation of missing creatinine dataset

A Competitive Particle Swarm Algorithm Based on Vector Angles for Multi-Objective Optimization

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