Biological survival optimization algorithm with its engineering and neural network applications

Likai Wang, Qingyang Zhang* (Corresponding Author), Xiangyu He, Shengxiang Yang, Shouyong Jiang* (Corresponding Author), Yongquan Dong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. This algorithm consists of two important courses, escape phase and adjustment phase. Specifically, in the escape phase, each search agent is required to update its location using the best, the worst and a neighboring individual of the population. The adjustment phase is implemented using the simplex algorithm for search better location of the worst agent within a small region. The effectiveness of the BSO is validated on the CEC2017 benchmark problems, three classical engineering structural problems and neural network training models. Simulation comparison results considering both convergence and accuracy simultaneously show that BSO has competitive performance compared with other state-of-the-art optimization techniques.

Original languageEnglish
Pages (from-to)6437–6463
Number of pages7
JournalSoft Computing
Early online date13 Feb 2023
Publication statusE-pub ahead of print - 13 Feb 2023


  • Biological survival optimizer
  • Engineering structural problem
  • Escape behavior
  • Neural network


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