Agent-based Architecture for Task Scheduling and Dynamic Orchestration Support

Othman, Sarah Ben; Ajmi, Inès; Quilliot, Alain

doi:10.1016/b978-1-78548-044-7.50005-1

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One chromosome is all that is needed for a mutation to produce a kid chromosome because it is an asexual operator. To prevent premature convergence, these operators allow the population's evolution to retain its random aspect [15]. A beneficial mutation confers a fitness benefit on the individual, increasing the likelihood of reproduction and the ability to pass on the advantageous characteristic to future generations.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancement of Genetic Algorithm by J.Zhang Applied to Tour Planning

2024

IJATCSE

View full text Add to dashboard Cite

Following widespread lockdowns, there has been a notable increase in people's desire to travel, leading to longer and more frequent trips. This trend has created a demand for customized itineraries and tour planning. Unfortunately, manual tour planning can be challenging to optimize, time-consuming, and increasingly complex as the number of locations increases. To automate and improve tour planning, optimization methods can be used, as they leverage algorithms to find efficient routes. The Genetic Algorithm (GA), an algorithm that mimics the course of natural evolution, is adept at navigating complex search spaces and finding optimal solutions, making it suitable for solving tour planning challenges. Building upon the work of J. Zhang (2021), this study aims to improve the performance of GA by enhancing the diversity of the population, removing redundant nodes, and reducing the execution time. Two simulators were created, one for each algorithm, to test their performance. The researchers conducted tests on both the existing and enhanced algorithms. This involved the utilization of several test data that contains coordinates of several cities in the Philippines. Based on the results, the enhanced algorithm showed better results compared to the existing algorithm. In conclusion, the enhanced algorithm performed better than the exiting algorithm

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Enhancement of Genetic Algorithm by J.Zhang Applied to Tour Planning

2024

IJATCSE

View full text Add to dashboard Cite

show abstract

Agent-based Architecture for Task Scheduling and Dynamic Orchestration Support

Cited by 1 publication

References 0 publications

Enhancement of Genetic Algorithm by J.Zhang Applied to Tour Planning

Enhancement of Genetic Algorithm by J.Zhang Applied to Tour Planning

Contact Info

Product

Resources

About