The Close Enough Traveling Salesman Problem (CETSP) is a well-known variant
of the classic Traveling Salesman Problem whereby the agent may complete its
mission at any point within a target neighborhood. Heuristics based on
overlapped neighborhoods, known as Steiner Zones (SZ), have gained attention in
addressing CETSPs. While SZs offer effective approximations to the original
graph, their inherent overlap imposes constraints on the search space,
potentially conflicting with global optimization objectives. Here we present
the Close Enough Orienteering Problem with Non-uniform Neighborhoods (CEOP-N),
which extends CETSP by introducing variable prize attributes and non-uniform
cost considerations for prize collection. To tackle CEOP-N, we develop a new
approach featuring a Randomized Steiner Zone Discretization (RSZD) scheme
coupled with a hybrid algorithm based on Particle Swarm Optimization (PSO) and
Ant Colony System (ACS) - CRaSZe-AntS. The RSZD scheme identifies sub-regions
for PSO exploration, and ACS determines the discrete visiting sequence. We
evaluate the RSZD's discretization performance on CEOP instances derived from
established CETSP instances, and compare CRaSZe-AntS against the most relevant
state-of-the-art heuristic focused on single-neighborhood optimization for
CEOP. We also compare the performance of the interior search within SZs and the
boundary search on individual neighborhoods in the context of CEOP-N. Our
results show CRaSZe-AntS can yield comparable solution quality with
significantly reduced computation time compared to the single-neighborhood
strategy, where we observe an averaged 140.44% increase in prize collection and
55.18% reduction of execution time. CRaSZe-AntS is thus highly effective in
solving emerging CEOP-N, examples of which include truck-and-drone delivery
scenarios.Comment: 26 pages, 10 figure