A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm

Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial optimization problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages such as long running time and easy stagnation s...

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Vydáno v:Mathematics (Basel) Ročník 10; číslo 6; s. 925
Hlavní autoři: Li, Shugang, Wei, Yanfang, Liu, Xin, Zhu, He, Yu, Zhaoxu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.03.2022
Témata:
ant colony algorithm
Ant colony optimization
Collaboration
Combinatorial analysis
Domains
Efficiency
Evolutionary algorithms
Food science
near-optimal path identification
Optimization algorithms
optimization speed
Path predictors
Pheromones
Prediction models
Quantitative analysis
Traveling salesman problem
Windows (intervals)
ISSN:2227-7390, 2227-7390
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Shrnutí:Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial optimization problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages such as long running time and easy stagnation still restrict its further wide application in many fields. In this study, a saltatory evolution ant colony optimization (SEACO) algorithm is proposed to increase the optimization speed. Different from the past research, this study innovatively starts from the perspective of near-optimal path identification and refines the domain knowledge of near-optimal path identification by quantitative analysis model using the pheromone matrix evolution data of the traditional ACO algorithm. Based on the domain knowledge, a near-optimal path prediction model is built to predict the evolutionary trend of the path pheromone matrix so as to fundamentally save the running time. Extensive experiment results on a traveling salesman problem library (TSPLIB) database demonstrate that the solution quality of the SEACO algorithm is better than that of the ACO algorithm, and it is more suitable for large-scale data sets within the specified time window. This means it can provide a promising direction to deal with the problem about slow optimization speed and low accuracy of the ACO algorithm.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math10060925