Real-time optimization of dynamic problems through distributed Embodied Evolution

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.