Affiliations: Integrated Group for Engineering Research, Universidade da Coruña, Spanish Galicia Autonomous Region, Spain
Correspondence:
[*]
Corresponding author: R.J. Duro, Escola Politecnica Superior, Campus de Esteiro, 15403, Ferrol, A Coruna, Spain. E-mail:[email protected]
Abstract: Several engineering optimization problems like routing,
freight transportation, exploration, or layout design in their more complex
and realistic versions present a series of characteristics that make them
very difficult to solve. Among these we find the absence of centralized
updated information about all the variables, due to the spread out nature of
the problems or lack of appropriate communications, or the dynamism of
real-time operation. In fact, most optimization approaches assume that the
problems they address are static, meaning that there is an optimal solution
that does not change in time, but this is not always the case and there are
problems that require following an optimum that changes in time. Distributed
population-based techniques, such as swarms, have provided promising results
in this context. They obtain a solution through the concurrent behavior of
several adequately constructed processing elements. However, constructing
these swarms is not straightforward, and most approaches have just mimicked
swarm behaviors found in nature, adapting them to particular problems. The
objective of this work is to study the application of a novel evolutionary
paradigm, distributed Embodied Evolution (dEE), to obtain heterogeneous
swarms that solve a set of realistic problems. In particular, we address
here non-separable dynamic fitness landscapes, where interdependences
between individuals imply that the contribution provided by one of them to
the whole depends on the behavior of the others. This study is carried out
applying a canonical version of dEE, which has been developed to generalize
the main features of this type of evolutionary paradigm. We analyze the
canonical dEE response in a series of scenarios of increasing complexity
related to two highly representative dynamic engineering problems: a Dynamic
Fleet Size and Mix Vehicle Routing Problem with Time Windows (DFSMVRPTW) and
a collective surveillance task with realistic location degradation.
