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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Published in:	Mathematics (Basel) Vol. 10; no. 6; p. 925
Main Authors:	Li, Shugang, Wei, Yanfang, Liu, Xin, Zhu, He, Yu, Zhaoxu
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.03.2022
Subjects:	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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Abstract	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.
AbstractList	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.
Author	Li, Shugang Wei, Yanfang Liu, Xin Yu, Zhaoxu Zhu, He
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SubjectTerms	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)
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Title	A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm
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