A fine-grained fast parallel genetic algorithm based on a ternary optical computer for solving traveling salesman problem

In this paper, we propose a fine-grained fast parallel genetic algorithm based on a ternary optical computer. Owing to the natural parallelizability of genetic algorithms, we take advantage of the characteristics of ternary optical computers, such as a large number of processor bits that can be reconfigured or allocated bit-by-bit, parallel computation, and low power consumption. The design and implementation of a parallel genetic algorithm on a ternary optical computer by solving the traveling salesman problem (TSP) is used as an example. Theoretical analysis and experimental results show that our proposed algorithm provides more efficient performance when the size of the members of the cities continuously increases according to the acceleration ratio measurement results. It also lays the theoretical foundation for the application of ternary optical computers in data-intensive computing.