Scheduling System for Multiple Self-driving Cars Using K-Means and Bio-inspired Optimization Algorithms

Silva, Clênio E.; César, Tiago S.; Gomes, Iago P.; Silva, Júnior A. R.; Wolf, Denis F.; Alves, Raulcézar; Souza, Jefferson R.

doi:10.1007/s42979-023-02053-z

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…2024, 13, 40 2 of 18 and recommendations. Second, the optimal tourist path is recommended by solving the traveling salesman problem (TSP) [8,9].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the K-Means Algorithm through a Genetic Algorithm Based on Survey and Social Media Tourism Objectives for Tourism Path Recommendations

Damos,

Zhu,

et al. 2024

IJGI

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Social media platforms play a vital role in determining valuable tourist objectives, which greatly aids in optimizing tourist path planning. As data classification and analysis methods have advanced, machine learning (ML) algorithms such as the k-means algorithm have emerged as powerful tools for sorting through data collected from social media platforms. However, traditional k-means algorithms have drawbacks, including challenges in determining initial seed values. This paper presents a novel approach to enhance the k-means algorithm based on survey and social media tourism data for tourism path recommendations. The main contribution of this paper is enhancing the traditional k-means algorithm by employing the genetic algorithm (GA) to determine the number of clusters (k), select the initial seeds, and recommend the best tourism path based on social media tourism data. The GA enhances the k-means algorithm by using a binary string to represent initial centers and to apply GA operators. To assess its effectiveness, we applied this approach to recommend the optimal tourism path in the Red Sea State, Sudan. The results clearly indicate the superiority of our approach, with an algorithm optimization time of 0.01 s. In contrast, traditional k-means and hierarchical cluster algorithms required 0.27 and 0.7 s, respectively.

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“…2024, 13, 40 2 of 18 and recommendations. Second, the optimal tourist path is recommended by solving the traveling salesman problem (TSP) [8,9].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the K-Means Algorithm through a Genetic Algorithm Based on Survey and Social Media Tourism Objectives for Tourism Path Recommendations

Damos,

Zhu,

et al. 2024

IJGI

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CMSV: a New Cloud Multi-Agents for Self-Driving Vehicles as a Services

Nasr

2024

J Grid Computing

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The development of autonomous vehicles has changed the way of transmitting goods to users, with the potential to improve road safety and efficiency. One of the effective issues of self-driving vehicles is path planning issue. In this paper, a new system is proposed for addressing the potential of using multi-agent systems (MAS) to address path planning problems in autonomous vehicles. The system is called Cloud Multi-Agents for Self-Driving Vehicles as A Services (CMSV). It is used to solve the dependent delivery problem, where the vehicle transmits the goods/deliveries, that are dependent on each other, to specific locations in ordering or with specific directions. The new system consists of three phases: (1) converting the problem into TSP sub problems according to deliveries dependency, (2) applying GDC to select the good solutions (3) applying multi-agent system to select the optimal solution. The proposed system is compared with systems that use a genetic algorithm, Ant Colony algorithm, Particle Swarm Optimization and The Harris hawk optimization (HHO) algorithm. From the findings, we find that the proposed system is more efficient than the other systems.

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Scheduling System for Multiple Self-driving Cars Using K-Means and Bio-inspired Optimization Algorithms

Cited by 2 publications

References 14 publications

Enhancing the K-Means Algorithm through a Genetic Algorithm Based on Survey and Social Media Tourism Objectives for Tourism Path Recommendations

Enhancing the K-Means Algorithm through a Genetic Algorithm Based on Survey and Social Media Tourism Objectives for Tourism Path Recommendations

CMSV: a New Cloud Multi-Agents for Self-Driving Vehicles as a Services

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