This paper presents a self--adaptive bacteria swarm optimization algorithm, and its application in a suite of optimization benchmark problems, where the self--adaptive algorithm outperformed in most cases the non adaptive version. The algorithm follows a methodology that uses some concepts included in the Evolution Strategies for the parameter control, allowing the algorithm to select online the best parameter set.
