Performance Analysis of a Distributed Steady-State Genetic Algorithm Using Low-Power Computers

In this chapter, we describe the implementation and evaluation of a distributed steady-state genetic algorithm on low-power computers. Specifically, we integrate the NVIDIA Jetson card and the ESP32 cards to form a master-slave model. NVIDIA Jetson card is the master, whereas the ESP32 cards are the slaves. We evaluated the distributed steady-state genetic algorithm on two challenging combinatorial problems: the n-Queens problem and the travelling salesman problem. To compare the performance of the distributed steady-state genetic algorithm on those well-known problems, we implemented a sequential steady-state genetic algorithm. The simulation results indicate that the distributed steady-state genetic algorithm can escape local minima in the travelling salesman problem; hence, the solutions have a better quality of fitness than the sequential genetic algorithm ones. In contrast, for the n-Queens problem, both genetic algorithms’ performance is remarkably similar.