Improved differential evolution based on mutation strategies

John Saveca, Zenghui Wang, Yanxia Sun

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

3 Citations (Scopus)


Differential Evolution (DE) has been regarded as one of the excellent optimization algorithm in the science, computing and engineering field since its introduction by Storm and Price in 1995. Robustness, simplicity and easiness to implement are the key factors for DE’s success in optimization of engineering problems. However, DE experiences convergence and stagnation problems. This paper focuses on DE convergence speed improvement based on introduction of newly developed mutation schemes strategies with reference to DE/rand/1 on account and tuning of control parameters. Simulations are conducted using benchmark functions such as Rastrigin, Ackley and Sphere, Griewank and Schwefel function. The results are tabled in order to compare the improved DE with the traditional DE.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings
EditorsYing Tan, Yuhui Shi, Qirong Tang
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319938141
Publication statusPublished - 2018
Event9th International Conference on Swarm Intelligence, ICSI 2018 - Shanghai, China
Duration: 17 Jun 201822 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10941 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Swarm Intelligence, ICSI 2018


  • Control parameters
  • Convergence speed
  • Differential Evolution
  • Mutation scheme

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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