Application of chaos discrete particle swarm optimization algorithm on pavement maintenance scheduling problem

Particle swarm optimization (PSO) is one of the most popular and successful optimization algorithms used for solving single objective and multi-objective optimization problems. It is found that the Multi objective particle swarm optimization (MOPSO) has ability to find the optimal solution quickly a...

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Vydáno v:Cluster computing Ročník 22; číslo Suppl 2; s. 4647 - 4657
Hlavní autoři: Ahmed, Kawther, Al-Khateeb, Belal, Mahmood, Maher
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.03.2019
Springer Nature B.V
Témata:
Algorithms
Computer Communication Networks
Computer Science
Discount rates
Flexible pavements
Genetic algorithms
Maintenance costs
Maintenance management
Multiple objective analysis
Operating Systems
Optimization techniques
Particle swarm optimization
Processor Architectures
Roads & highways
Scheduling
Traffic
Velocity
Pavement maintenance
Chaotic mapping
Binary PSO
Multi-objective optimization
Particle swarm optimization
ISSN:1386-7857, 1573-7543
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Shrnutí:Particle swarm optimization (PSO) is one of the most popular and successful optimization algorithms used for solving single objective and multi-objective optimization problems. It is found that the Multi objective particle swarm optimization (MOPSO) has ability to find the optimal solution quickly and more efficient than other optimization algorithms. In this paper, a discrete (binary) MOPSO with chaos methods is developed and applied to pavement maintenance management. The main objective of this research is to find optimal maintenance and rehabilitation plan for flexible pavement with minimum maintenance cost and maximum pavement performance. This research is the first attempt to combine the crossover operation with velocity and position with multi objective PSO algorithm. The results show that the improvements in pavement performance and cost objectives are 94.65 and 54.01% respectively, while the improvement in execution time is 99.9%. In addition, it is found that the developed algorithm is able to converge to the optimal solution quickly, comparing with another PSO algorithm.
Bibliografie:ObjectType-Article-1
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-018-2239-3