A dynamic space reduction ant colony optimization for capacitated vehicle routing problem

As a typical meta-heuristic algorithm, ant colony optimization (ACO) has achieved good results in solving discrete combinatorial optimization problems. However, it suffers from poor solutions and the drawback of easily being trapped in local optima. This paper presents a new type of ACO called “dyna...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 26; H. 17; S. 8745 - 8756
Hauptverfasser: Cai, Jinsi, Wang, Peng, Sun, Siqing, Dong, Huachao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022
Schlagworte:
Artificial Intelligence
Computational Intelligence
Control
Engineering
Mathematical Logic and Foundations
Mechatronics
Optimization
Robotics
Ant colony optimization
Discrete combinatorial optimization problem
Capacitated vehicle routing problem
Evolutionary algorithm
ISSN:1432-7643, 1433-7479
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Zusammenfassung:As a typical meta-heuristic algorithm, ant colony optimization (ACO) has achieved good results in solving discrete combinatorial optimization problems. However, it suffers from poor solutions and the drawback of easily being trapped in local optima. This paper presents a new type of ACO called “dynamic space reduction ant colony optimization” (DSRACO) to solve the capacitated vehicle routing problem, which is a typical nondeterministic polynomial-hard optimization problem. In DSRACO, ACO is integrated with a unique dynamic space reduction method, an elite enhanced mechanism, and large-scale neighborhood search methods to improve the quality of the solution. The performance of DSRACO is evaluated using 73 well-known benchmark instances in comparison with ACO and three other cutting-edge algorithms. The experimental results show that DSRACO can solve CVRP with a satisfactory result.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-022-07198-2