Title :
A memetic algorithm for uncertain Capacitated Arc Routing Problems
Author :
Juan Wang ; Ke Tang ; Xin Yao
Author_Institution :
Sch. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China
Abstract :
The Capacitated Arc Routing Problem (CARP) is a widely investigated classic combinatorial optimization problem. Being a deterministic model, it is far away from the real world. A more practical problem model of CARP is the Uncertain CARP (UCARP), with the objective of finding a robust solution which performs well in all possible environments. There exist few algorithms for UCARP in previous work. In this paper, a Memetic Algorithm (MA) and its modified version in time consumption for UCARP are proposed. Experimental results on two benchmark test sets show that with an integrated fitness function and a large step-size local search operator, the new MAs show excellent ability to find robust solutions for UCARP. We also present a less time-consuming version of our MA which shows significant advantages in time consumption.
Keywords :
deterministic algorithms; genetic algorithms; network routing; MA; UCARP; benchmark test sets; classic combinatorial optimization problem; deterministic model; integrated fitness function; memetic algorithm; step-size local search operator; time consumption; uncertain CARP; uncertain capacitated arc routing problems; Maintenance engineering; Memetics; Optimization; Robustness; Sociology; Statistics; Vehicles; UCARP; evolutionary algorithm; robust optimization;
Conference_Titel :
Memetic Computing (MC), 2013 IEEE Workshop on
Conference_Location :
Singapore
DOI :
10.1109/MC.2013.6608210