<![CDATA[Choosing a good initial point matters as much as the optimisation method itself when tuning a biotechnological process, yet the two most common approaches to getting there each carry a cost: fuzzy-set methods give a direct, transparent solution but scale poorly once the decision space grows large, while random-search methods scale well but do not on their own guarantee a well-chosen starting point. This paper develops a combined algorithm that pairs a random-search-with-back-step (RSBS) method for locating a good initial point with a fuzzy-sets-theory (FST) optimisation stage, so that the random search narrows the search region before the fuzzy method is asked to discretise it — directly addressing the scale limitation that otherwise limits fuzzy optimisation. The combined algorithm is applied to the initial-condition and feeding-rate optimal control problem for the fed-batch biotransformation of whey by a strain of Kluyveromyces marxianus var. lactis MC5 in a laboratory stirred-tank bioreactor, raising the optimisation.]]>
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