Distributed evolutionary algorithms and their models: a survey of the state-of-the-art

Yue Jiao Gong, Wei Neng Chen, Zhi Hui Zhan, Jun Zhang, Yun Li, Qingfu Zhang, Jing Jing Li

Research output: Contribution to journalReview articlepeer-review

199 Citations (Scopus)


The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish.

Original languageEnglish
Pages (from-to)286-300
Number of pages15
JournalApplied Soft Computing Journal
Publication statusPublished - 4 Jun 2015


  • coevolutionary computation
  • distributed evolutionary computation
  • evolutionary algorithms
  • global optimization
  • multiobjective optimization


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