  Swarm Intelligence is a metaheuristic optimization approach that has become
very predominant over the last few decades. These algorithms are inspired by
animals' physical behaviors and their evolutionary perceptions. The simplicity
of these algorithms allows researchers to simulate different natural phenomena
to solve various real-world problems. This paper suggests a novel algorithm
called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by
the searching behavior of donkeys. The algorithm imitates transportation
behavior such as searching and selecting routes for movement by donkeys in the
actual world. Two modes are established for implementing the search behavior
and route-selection in this algorithm. These are the Smuggler and Donkeys. In
the Smuggler mode, all the possible paths are discovered and the shortest path
is then found. In the Donkeys mode, several donkey behaviors are utilized such
as Run, Face & Suicide, and Face & Support. Real world data and applications
are used to test the algorithm. The experimental results consisted of two
parts, firstly, we used the standard benchmark test functions to evaluate the
performance of the algorithm in respect to the most popular and the state of
the art algorithms. Secondly, the DSO is adapted and implemented on three
real-world applications namely; traveling salesman problem, packet routing, and
ambulance routing. The experimental results of DSO on these real-world problems
are very promising. The results exhibit that the suggested DSO is appropriate
to tackle other unfamiliar search spaces and complex problems.
