Swarm intelligence systems for transportation engineering: Principles and applications

Teodorović, Dušan

doi:10.1016/j.trc.2008.03.002

Cited by 160 publications

(78 citation statements)

References 38 publications

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“…Colonies such as ants and bees have instinct ability known as swarm intelligence [11], [12]. This behavior enables the colonies of insects to solve problems beyond capability of individual members and interacting primitively amongst members of the group.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analyzes of Bee Colony Split-Plot Algorithm

Hidayat¹,

Sugioko²

2015

IJIET

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Abstract-The Bee Colony Algorithms is a popular algorithm in 2006, in which this algorithm was an approach to solve problems using bee's behavior. The Bee Colony Algorithm has a quite simple flow, therefore, many studies have carry out modifications for specific problems. This study will modify the Bee Colony Algorithm to become more resembled with the Bee's behavior, using the split-plot design principle.This study is aiming to look at the performance of the Bee Colony split-plot Algorithm based on the bee's group behavior. In this study the performance of the Bee Colony algorithm is tested using the case study of the flow shop scheduling with the due date of 3 (three) cases, aiming to minimize the amount of late jobs. This test will be compared with the genetic algorithm. The performance of some groups and the computing time of the Bee Colony's Algorithm split-plot will be analyzed using the flow shop problem with the aim to minimize the makespan. The study finding has shown that the Bee Colony Algorithm split-plot as proposed has resulted in a performance that resembles the genetic algorithm for the second and the third cases, whereas for the first case, the algorithm bee colony has a better performance with an average of 2,4615 late jobs and for the genetic 2,615 jobs.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Analyzes of Bee Colony Split-Plot Algorithm

Hidayat¹,

Sugioko²

2015

IJIET

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“…These simple agents can solve complex tasks by interacting with each other. Swarm algorithms model this interactive behavior [17]. Several different types of swarm algorithms were looked at including ant-based, bee-based, firefly-based and particle-based.…”

Section: Swarm Algorithmsmentioning

confidence: 99%

“…Ant-based Ant colony swarm theory is described by Teodorović et al [17]. In its journey, each ant leaves a pheromone trail behind.…”

Section: Swarm Algorithmsmentioning

confidence: 99%

“…Bees use this information to choose the optimum food source to visit. Bee colony swarm is described by Teodorović et al [17]. Initially the bees use a random walk model to determine a partial solution.…”

mentioning

confidence: 99%

“…This set of algorithms is described by Teodorović et al [17]. A particle remembers its best solution and that of the best solution obtained by any other particle.…”

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confidence: 99%

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Time-Ants: An innovative temporal and spatial ant-based vehicular Routing Mechanism

Doolan

Muntean

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Abstract-Increasing amounts of time is wasted due to traffic congestion in both developed and developing countries. This has severe negative effects, including drivers stress due to increased time pressure, reduced usage efficiency of trucks and other commercial vehicles, and increased gas emissions--responsible for climate change and air pollution affecting population health in densely populated areas. As existing centralized approaches were neither efficient, nor scalable, there is a need for alternative approaches. Social insects provide many solutions for dealing with decentralized problems. For instance ants choose their routes based on pheromones left by previous ants. However, Ant Colony Optimization is not directly applicable to vehicle routing, as routing the vehicles to the same road would cause traffic congestion. Yet, the traffic is broadly similar from work-to work-day. This paper introduces an ant-colony optimizationbased algorithm called Time-Ants. Time-Ants considers that an amount of "pheromone" or a traffic rating is assigned to each road at any given time in the day. Using an innovative algorithm the vehicle's routes are chosen based on these traffic ratings, aggregated in time. After several iterations this results in a global optimum for the traffic system. Bottlenecks are identified and avoided by machine learning. Time-Ants outperforms another leading algorithm by up to 19% in terms of percentage of vehicles to reach the destination within a given time-frame.

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Computational Intelligence

Nieves

2022

Smart Urban Logistics

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Swarm intelligence systems for transportation engineering: Principles and applications

Cited by 160 publications

References 38 publications

Performance Analyzes of Bee Colony Split-Plot Algorithm

Performance Analyzes of Bee Colony Split-Plot Algorithm

Time-Ants: An innovative temporal and spatial ant-based vehicular Routing Mechanism

Computational Intelligence

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