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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Published in:	Soft computing (Berlin, Germany) Vol. 26; no. 17; pp. 8745 - 8756
Main Authors:	Cai, Jinsi, Wang, Peng, Sun, Siqing, Dong, Huachao
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022
Subjects:	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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Abstract	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.
AbstractList	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.
Author	Wang, Peng Dong, Huachao Cai, Jinsi Sun, Siqing
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Title	A dynamic space reduction ant colony optimization for capacitated vehicle routing problem
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