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

Shugang Li, Yanfang Wei, Xin Liu, He Zhu and Zhaoxu Yu
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Shugang Li: School of Management, Shanghai University, Shanghai 200444, China
Yanfang Wei: School of Management, Shanghai University, Shanghai 200444, China
Xin Liu: School of Management, Shanghai University, Shanghai 200444, China
He Zhu: School of Management, Shanghai University, Shanghai 200444, China
Zhaoxu Yu: Department of Automation, East China University of Science and Technology, Shanghai 200237, China

Mathematics, 2022, vol. 10, issue 6, 1-22

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.

Keywords: ant colony algorithm; traveling salesman problem; near-optimal path identification; optimization speed (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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