A multi-crossover and adaptive island based population algorithm for solving routing problems

Eneko OSABA; Enrique ONIEVA; Roberto CARBALLEDO; Fernando DIAZ; Asier PERALLOS; Xiao ZHANG

doi:10.1631/jzus.C1300184

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A multi-crossover and adaptive island based population algorithm for solving routing problems

计算机与自动化 | Updated：2022-12-09

- A multi-crossover and adaptive island based population algorithm for solving routing problems
- A multi-crossover and adaptive island based population algorithm for solving routing problems
- 信息与电子工程前沿（英文） 2013年14卷第11期页码：815-821
- Affiliations：
  
  Deusto Institute of Technology (DeustoTech), University of Deusto, Bilbao 48007, Spain
- Author bio：
  
  [ "Eneko OSABA, E-mail: e.osaba@deusto.es" ]
  [ "Enrique ONIEVA, E-mail: enrique.onieva@deusto.es" ]
  [ "Roberto CARBALLEDO, E-mail: roberto.carballedo@deusto.es" ]
  [ "Fernando DIAZ, E-mail: fernando.diaz@deusto.es" ]
  [ "Asier PERALLOS, E-mail: perallos@deusto.es" ]
  [ "Xiao ZHANG, E-mail: xiao.zhang@deusto.es" ]
- Funds：
- DOI：10.1631/jzus.C1300184
  中图分类号： TP273
- 收稿：2013-07-09，
  
  修回：2013-10-15，
  
  纸质出版：2013-11
- Accepted：
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A multi-crossover and adaptive island based population algorithm for solving routing problems[J]. 信息与电子工程前沿（英文）, 2013,14(11):815-821.

Eneko OSABA, Enrique ONIEVA, Roberto CARBALLEDO, et al. A multi-crossover and adaptive island based population algorithm for solving routing problems[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(11): 815-821.
A multi-crossover and adaptive island based population algorithm for solving routing problems[J]. 信息与电子工程前沿（英文）, 2013,14(11):815-821. DOI： 10.1631/jzus.C1300184.

Eneko OSABA, Enrique ONIEVA, Roberto CARBALLEDO, et al. A multi-crossover and adaptive island based population algorithm for solving routing problems[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(11): 815-821. DOI： 10.1631/jzus.C1300184.

摘要

Abstract

We propose a multi-crossover and adaptive island based population algorithm (MAIPA). This technique divides the entire population into subpopulations

or demes

each with a different crossover function

which can be switched according to the efficiency. In addition

MAIPA reverses the philosophy of conventional genetic algorithms. It gives priority to the autonomous improvement of the individuals (at the mutation phase)

and introduces dynamism in the crossover probability. Each subpopulation begins with a very low value of crossover probability

and then varies with the change of the current generation number and the search performance on recent generations. This mechanism helps prevent premature convergence. In this research

the effectiveness of this technique is tested using three well-known routing problems

i.e.

the traveling salesman problem (TSP)

capacitated vehicle routing problem (CVRP)

and vehicle routing problem with backhauls (VRPB). MAIPA proves to be better than a traditional island based genetic algorithm for all these three problems.

关键词

Keywords

references

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