Swarm intelligence (SI) techniques are population based algorithms that are inspired by the social behavior of animal.
Hi, these kind of techniques are easy to implement. The performance on a given problem can be evaluated by the number of function evaluations.
1.- Computational cost: SI-based meta-heuristics are relatively easy methods to implement.
Normally this type of algorithms performs searches in continuous spaces, and two approaches can be used to use them in discrete spaces: 1) Modify the operators that update the particles in the swarm, or 2) to define the scheme to map a candidate solution of the problem in a particle of the swarm.
These approaches impact the performance of an SI-based method.
2.- Performance metrics: This depends on what you want to measure: time, space, precision, for example. It also depends on the problem that is being solved.
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