Improving Performance of Genetic Algorithms for Transportation Systems: Case of Parallel Genetic Algorithms

Abstract

Genetic algorithms (GAs) can be the tool of choice especially for optimizing combinatorial and complex problems in transport and infrastructure systems such as traffic signal control, pavement rehabilitation and design, and transit service scheduling. This paper presents an overview of different techniques to improve performance of GAs, with particular emphasis on parallel GAs (PGAs). Results are presented from applications of a simple GA (SGA) and a migration PGAs on a traffic control problem, a benchmark GA–difficult, and benchmark GA–easy problem. For all problems, savings in computation resources were realized when PGA was used. Advantages of PGAs are more pronounced for complex and difficult (deceptive) problems. On a difficult problem tested in this research, a PGA with four subpopulations was 7 times more efficient than a serial one, and a PGA with eight subpopulations was more than 18 times more efficient. With smaller and less complex problems, the impact of parallelism is less dramatic when the computation resources are limited. Use of parallel GAs does not reduce the importance of seeking efficient problem-specific operators and parameter values, but does magnify the effectiveness of such choices and increase the range of options available. The advantages PGAs offer mean more efficient and faster optimization for many applications in civil infrastructure design, operating management, and maintenance projects.