A New Hybrid Genetic Algorithm and Particle Swarm Optimization

Hachimi, Hanaâ; Ellaia, Rachid; Hami, Abdelkhalak El

doi:10.4028/www.scientific.net/kem.498.115

Cited by 14 publications

(10 citation statements)

References 21 publications

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“…Many algorithms can solve similar problems. Given the advantages of the PSO-GA algorithm, like simple operations and fast convergences (Hachimi et al 2012), we use it to solve the problem.…”

Section: = ∑ ∑ × =1 =1mentioning

confidence: 99%

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Two-Stage Consensus Reaching Process for Two-Sided Matching Based on the Cloud Model in Large Sharing Platform

Tong¹,

Zhu²,

Tan³

2021

Preprint

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The sharing economy plays an important role in economic development, and the sharing platform is the core point in the sharing economy. However, the large-scale sharing platform results in low-efficiencies and weak matching. To address the problem, we design a two-stage consensus reaching process for resources sharing in platform. Firstly, considering the time-consuming process of generating satisfaction, we design a new method to generate satisfaction based on large-scale mixed historical data, the cloud model is used to unify the mixed uncertain information. Then, we design a two-stage consensus reaching process to realize the stable matching. For the first stage, we maximize the total consensus of the two-sided individuals. For the second stage, to realize stable sharing, we focus on the individuals’ requirement of consensus, the platform’s strategies, such as discount and scheduling, are used to adjust their consensus. Finally, considering the hierarchy of the two-stage consensus reaching process, we establish bi-level programming to embody the features. We design an improved algorithm to deal with bi-level programming. Also, an industrial internet platform is used as an example to verify the method and algorithm.

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Section: = ∑ ∑ × =1 =1mentioning

confidence: 99%

“…Then, the discount is a continuing variable. As a simple and economic concept, with low computational cost, PSO has been shown to optimize successfully a wide range of continuous optimization problems (Hachimi et al 2012). In addition, PSO has few control parameters, fast searching speed, and high efficiency.…”

Section: Improved Pso-gamentioning

confidence: 99%

Two-Stage Consensus Reaching Process for Two-Sided Matching Based on the Cloud Model in Large Sharing Platform

Tong¹,

Zhu²,

Tan³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The new individual can be considered a chromosome in the case of GA or called a particle in the case of PSO. N individuals are sorted by fitness, and the best N individuals are entered into the GA model to make N individuals new by crossover [12].…”

Section: Hgapso Attribute Selectionmentioning

confidence: 99%

“…The following is the pseudo code notation of the hybrid genetic algorithm and particle swarm optimization [12]. Equation 6 illustrates that the new velocity of each particle is updated with the previous velocity (V id ), the best location in the particle population ( P id ) and the best global location (P gd ).…”

Section: Hgapso Attribute Selectionmentioning

confidence: 99%

“…The velocity particles in each dimension are sandwiched using V max which is arranged into certain blocks of the search space for each dimension i. Equation 7shows that each particle (X id ) is updated during the search for the best solution [ 12 ]. Figure 4 shows a schematic HGAPSO method by setting the initial population as 4N.…”

Section: Hgapso Attribute Selectionmentioning

confidence: 99%

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Application of Hybrid Ga-Pso to Improve the Performance of Decision Tree C5.0

Nur¹,

Suyono²,

Aswin³

2020

kursor

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Data mining is a data extraction process with large dimensions and information with the aim of obtaining information as knowledge to make decisions. Problems in the data mining process often occur in high-dimensional data processing. The solution to handling problems in high-dimensional data is to apply the hybrid genetic algorithm and particle swarm optimization (HGAPSO) method to improve the performance of the C5.0 decision tree classification model to make decisions quickly, precisely and accurately on classification data. In this study, there were 3 datasets sourced from the University of California, Irvine (UCI) machine learning repositories, namely lymphography, vehicle, and wine. The HGAPSO algorithm combined with the C5.0 decision tree testing method has the optimal accuracy for processing highdimensional data. The lymphography and vehicle data obtained an accuracy of 83.78% and 71.54%. The wine dataset has an accuracy of 0.56% lower than the conventional method because the data dimensions are smaller than the lymphography and vehicle dataset.

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Combined Minimum Spanning Tree and Particle Swarm Optimization for the Design of the Cable Layout in Offshore Wind Farms

Mokhi

Addaim²

2021

Intelligent Computing and Innovation on Data Science

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A New Hybrid Genetic Algorithm and Particle Swarm Optimization

Cited by 14 publications

References 21 publications

Two-Stage Consensus Reaching Process for Two-Sided Matching Based on the Cloud Model in Large Sharing Platform

Two-Stage Consensus Reaching Process for Two-Sided Matching Based on the Cloud Model in Large Sharing Platform

Application of Hybrid Ga-Pso to Improve the Performance of Decision Tree C5.0

Combined Minimum Spanning Tree and Particle Swarm Optimization for the Design of the Cable Layout in Offshore Wind Farms

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