  Informationization is a prevailing trend in today's world. The increasing
demand for information in decision-making processes poses significant
challenges for investigation activities, particularly in terms of effectively
allocating limited resources to plan investigation programs. This paper
addresses the investigation path planning problem by formulating it as a
multi-traveling salesman problem (MTSP). Our objective is to minimize costs,
and to achieve this, we propose a chaotic artificial fish swarm algorithm based
on multiple population differential evolution (DE-CAFSA). To overcome the
limitations of the artificial fish swarm algorithm, such as low optimization
accuracy and the inability to consider global and local information, we
incorporate adaptive field of view and step size adjustments, replace random
behavior with the 2-opt operation, and introduce chaos theory and sub-optimal
solutions to enhance optimization accuracy and search performance.
Additionally, we integrate the differential evolution algorithm to create a
hybrid algorithm that leverages the complementary advantages of both
approaches. Experimental results demonstrate that DE-CAFSA outperforms other
algorithms on various public datasets of different sizes, as well as showcasing
excellent performance on the examples proposed in this study.

Neural and Evolutionary Computing