One parameter differential evolution (OPDE) for numerical benchmark problems

Y. Kang, T. O. Ting, Xin She Yang, Shi Cheng

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings
Number of pages8
EditionPART 1
Publication statusPublished - 2013
Event4th International Conference on Advances in Swarm Intelligence, ICSI 2013 - Harbin, China
Duration: 12 Jun 201215 Jun 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7928 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th International Conference on Advances in Swarm Intelligence, ICSI 2013


  • Benchmark problems
  • one parameter differential evolution
  • parameter sensitivity analysis


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