01556413

Component

In this paper the authors extend the standard meta-description for genetic algorithms with a simple non-trivial parallel implementation. The authors' work is chiefly concerned with the development of a straightforward way for engineers to modify existing genetic algorithm implementations for real industrial or scientific problems to make use of commonly available hardware resources without completely reworking complex, useful and useable codes. The authors present their framework and computational results comparing small scale parallelization for a classical transportation related combinatorial optimization problem – the traveling salesman problem with a standard sequential genetic algorithm implementation. The authors' empirical analysis shows that this simple extension can lead to considerable solution improvements. Next, the authors tested their assumptions that the results are typical and that the method is easily implemented by an engineer not initially familiar with genetic algorithms by implementing the toolkit for another classic scheduling problem.

This paper was sponsored by TRB committee ADB30 Transportation Network Modeling.

01550057

15-1361

English

18p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; References; Tables

Combinatorial analysis; Genetic algorithms; Logistics; Transportation planning

Planning and Forecasting; Transportation (General); I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2015 Paper #15-1361

TRIS, TRB, ATRI

Dec 30 2014 12:31PM