Final Remarks
Nowadays, bio-inspired algorithms have become a very important tool to solve engineering problems. Given that using classic optimization algorithms often implies computing and iteratively evaluating Hessians and gradients, the computational cost of this kind of algorithms—in some cases—may be excessively high. On the other hand, calculations of bio-inspired algorithms involve simple operations, most of them being linear equations. Furthermore, bio-inspired algorithms are easy to parallelize using a “divide and conquer” strategy, in which each individual in the population of the algorithm can be seen as a subprocess of the whole bio-inspired system. That means, each individual can be treated as a thread to solve ...
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