TY - GEN
T1 - One parameter differential evolution (OPDE) for numerical benchmark problems
AU - Kang, Y.
AU - Ting, T. O.
AU - Yang, Xin She
AU - Cheng, Shi
PY - 2013
Y1 - 2013
N2 - Differential Evolution (DE) can be simplified in the sense that the number of existing parameter is decreased from two parameters to only one parameter. We eliminate the scaling factor, F, and replace this by a uniform random number within [0, 1]. As such, it is easy to tune the crossover rate, CR, through parameter sensitivity analysis. In this analysis, the algorithm is run for 50 trials from 0.1 to 1.0 with a step increment of 0.1 on 23 benchmark problems. Results show that using the optimal CR, there is room for improvement in some of the benchmark problems. With the advantage and simplicity of a single parameter, it is significantly easier to tune this parameter and thus take the full advantage of the algorithm. The proposed algorithm here has a significant benefit when applied to real-world problems as it saves time in obtaining the best parameter setting for optimal performance.
AB - Differential Evolution (DE) can be simplified in the sense that the number of existing parameter is decreased from two parameters to only one parameter. We eliminate the scaling factor, F, and replace this by a uniform random number within [0, 1]. As such, it is easy to tune the crossover rate, CR, through parameter sensitivity analysis. In this analysis, the algorithm is run for 50 trials from 0.1 to 1.0 with a step increment of 0.1 on 23 benchmark problems. Results show that using the optimal CR, there is room for improvement in some of the benchmark problems. With the advantage and simplicity of a single parameter, it is significantly easier to tune this parameter and thus take the full advantage of the algorithm. The proposed algorithm here has a significant benefit when applied to real-world problems as it saves time in obtaining the best parameter setting for optimal performance.
KW - Benchmark problems
KW - one parameter differential evolution
KW - parameter sensitivity analysis
UR - http://www.scopus.com/inward/record.url?scp=84884846616&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38703-6_51
DO - 10.1007/978-3-642-38703-6_51
M3 - Conference Proceeding
AN - SCOPUS:84884846616
SN - 9783642387029
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 431
EP - 438
BT - Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings
T2 - 4th International Conference on Advances in Swarm Intelligence, ICSI 2013
Y2 - 12 June 2012 through 15 June 2012
ER -