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An Improved Genetic Algorithm for Vehicle Routing Problem

Zongyan Xu, Haihua Li, Yilin Wang

Open publisher page 13 citations

Abstract

The Vehicle Routing Problem (VRP) is a typical combinational optimization problem. Genetic Algorithm (GA) is one of the methods used to solve VRP. By incorporating Simulated Annealing (SA) into GA, an improved genetic algorithm is proposed to solve the classical VRP in this paper. To improve the computational efficiency of GA, an improved inversion mutation operation is also exploited so that more parents' excellent performance can be inherited by off-springs. A measure, individual concentration, is introduced to evaluate population diversity. Once population diversity is below a given level, the algorithm is switched to SA, which could avoid the drawback of premature convergence in GA. Some experimental data show the effectiveness of the algorithm and authenticate the search efficiency and solution quality of the algorithm.

About this research paper

What this paper is about

The Vehicle Routing Problem (VRP) is a typical combinational optimization problem. Genetic Algorithm (GA) is one of the methods used to solve VRP. By incorporating Simulated Annealing (SA) into GA, an improved genetic algorithm is proposed to solve the classical VRP in this paper. To improve the computational efficiency of GA, an improved inversion mutation operation is also exploited so that more parents' excellent performance can be inherited by off-springs. A measure, individual concentration, is introduced to evaluate population diversity. Once population diversity is below a given level, the algorithm is switched to SA, which could avoid the drawback of premature convergence in GA. Some experimental data show the effectiveness of the algorithm and authenticate the search efficiency and solution quality of the algorithm.

Why it matters

OpenAlex reports 13 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

The Vehicle Routing Problem (VRP) is a typical combinational optimization problem. Genetic Algorithm (GA) is one of the methods used to solve VRP. By incorporating Simulated Annealing (SA) into GA, an improved genetic algorithm is proposed to solve the classical VRP in this paper. To improve the computational efficiency of GA, an improved inversion mutation operation is also exploited so that more parents' excellent performance can be inherited by off-springs. A measure, individual concentration, is introduced to evaluate population diversity. Once population diversity is below a given level, the algorithm is switched to SA, which could avoid the drawback of premature convergence in GA. Some experimental data show the effectiveness of the algorithm and authenticate the search efficiency and solution quality of the algorithm.

Key concepts: Vehicle routing problem, Simulated annealing, Genetic algorithm, Computer science, Mathematical optimization, Algorithm, Population, Premature convergence

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