Engineers constantly encounter technological problems which are becoming increasingly complex. These problems may be encountered in different domains such as transport, telecommunications, genomics, technology for the healthcare sector and electronics. The given problem can often be expressed as one which could be solved by optimization. Within this process of optimization, one or several “objective functions” are defined. The aim of this process is to minimize the “objective function” in relation to all parameters concerned. Apart from problems of optimization, i.e. the problem’s objective function which is part of this topic (e.g. improving the shape of a ship, reducing polluting emissions, obtaining a maximum profit), a large number of other situations of indirect optimization can be encountered (e.g. identification of a model or the learning process of a new cognitive system). When looking at this issue from the angle of available methods used to resolve a given problem, a large variety of methods can be considered. On the one hand, there are “classic methods” that rely purely on mathematics, but impose strict application conditions. On the other hand, digital methods that could be referred to as “heuristic” do not try to find an ideal solution but try to obtain a solution in a given time available for the calculation. Part of the latter group of methods is “metaheuristics”, which emerged in the 1980s. Metaheuristics has many similarities with physics, biology ...

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