An information-theoretic-based evolutionary approach for the dynamic search path planning problem

Abstract

A new information-theoretic-based evolutionary approach is proposed to solve the dynamic search path planning problem. Path planning is achieved using an open-loop model with anticipated feedback while dynamically capturing incoming new requests and real action outcomes/observations as exogenous events, to timely adjust search path plans using coevolution. The approach takes advantage of objective function separability and conditional observation probability independence to efficiently minimize expected system entropy, lateness and travel/discovery time respectively. Computational results clearly show the value of the approach in comparison to a myopic heuristics over various problem instances.