A Review of Firefly Algorithms for Path Planning, Vehicle Routing and Traveling Salesman Problems

Chandrawati, T. Brenda; Sari, Riri Fitri

doi:10.1109/icon-eei.2018.8784312

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…The modified Firefly algorithm overcomes the convergence slowness of the basic Firefly algorithm. This is helpful in 3D underwater planning as we can achieve faster optimization [87].…”

Section: Firefly Algorithmmentioning

confidence: 99%

A Survey of Path Planning Algorithms for Mobile Robots

et al. 2021

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Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.

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“…The modified Firefly algorithm overcomes the convergence slowness of the basic Firefly algorithm. This is helpful in 3D underwater planning as we can achieve faster optimization [87].…”

Section: Firefly Algorithmmentioning

confidence: 99%

A Survey of Path Planning Algorithms for Mobile Robots

et al. 2021

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“…This work compared with PSO algorithm and the results showed that the path was reduced to 30%. Chandrawati et al [48,54] discussed the application of firefly. The path planning used a firefly in many papers.…”

Section: Firefly Algorithmmentioning

confidence: 99%

Assessment and Review of the Reactive Mobile Robot Navigation

Yosif¹,

Mahmood²,

Al-Khayyt³

2021

(AREJ)

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Nowadays, the mobile robot can be seen in different fields of engineering and science. The mobile robot can do some tasks that are so difficult or very risky to be performed by a human. Most of the works currently focus on implementing artificially intelligent algorithms and other algorithms that depend on the behaviour of nature. These approaches have been used in mobile robot navigation along uncertain manner. Mobile robot navigation strategies can be divided into two approaches: the classical approach and reactive approach. The classical approach related to static environment, whiles the reactive navigation is based on an unstructured environment. Path planning is one of the most important parts of the navigation system. In this paper, review and assessment of path planning strategies that can concern with the reactive approach are discussed, because it deal with the problem of dynamic environment. Numerous reactive methods have been introduced. Most of these presented works were concerned with simulation and a few of them have shown experimental implementation. Many papers tried to make a combination between two algorithms or more to increase the efficiency. It is concluded that reactive algorithms need more learning phases, complex in design, and require large memory storage.

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“…Data Penelitian ini dilakukan bertujuan untuk menganalisis performansi algoritma firefly dan tabu search (FATS) dalam optimasi rute angkutan kota dengan menguji parameter algoritma firefly dalam menentukan parameter optimal. Pada penelitian sebelumnya, algoritma firefly dapat digunakan untuk untuk menyelesaikan permasalahan perencanaan jalur, masalah rute kendaraan, dan masalah salesman keliling yang berupa optimasi jalur, waktu dan jarak [14]. Tabu search digunakan untuk menemukan solusi optimal dari masalah optimasi kombinatorial, seperti travelling salesman problem dalam pencarian jarak terpendek [15], graph coloring problems [16], scheduling problem [17], dan vehicle routing problem [18].…”

Section: Metode Penelitianunclassified

Determination of Optimal Public Transportation Routes Using Firefly and Tabu Search Algorithms

Mansur

Widowati

Imrona

2020

RESTI

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Traffic congestion problems generally caused by the increasing use of private vehicles and public transportations. In order to overcome the situation, the optimization of public transportation’s route is required particularly the urban transportation. In this research, the performance analysis of Firefly and Tabu Search algorithm is conducted to optimize eleven public transportation’s routes in Bandung. This optimization aims to increase the dispersion of public transportation’s route by expanding the scope of route that are crossed by public transportation so that it can reach the entire Bandung city and increase the driver’s income by providing the passengers easier access to public transportations in order to get to their destinations. The optimal route is represented by the route with most roads and highest number of incomes. In this research, the comparison results between the reference route and the public transportation’s optimized route increasing the dispersion of public transportation’s route to 60,58% and increasing the driver’s income to 20,03%.

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A Review of Firefly Algorithms for Path Planning, Vehicle Routing and Traveling Salesman Problems

Cited by 9 publications

References 9 publications

A Survey of Path Planning Algorithms for Mobile Robots

A Survey of Path Planning Algorithms for Mobile Robots

Assessment and Review of the Reactive Mobile Robot Navigation

Determination of Optimal Public Transportation Routes Using Firefly and Tabu Search Algorithms

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