Biological survival optimization algorithm with its engineering and neural network applications

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.