Research and Implementation of Parallel Artificial Fish Swarm Algorithm Based on Ternary Optical Computer

Artificial fish swarm algorithm (AFS) is used in the field of function optimization problems widely. The traditional AFS algorithm has some problems such as long time to find the optimal solution and easy to fall into local optimality at the later stage of the search. We investigate design solutions and methods to implement parallel AFS algorithms by taking advantage of the large number of TOC processor bits and the easy scalability of processor bits. Firstly, we find out the parallel part of the algorithm by analyzing the traditional AFS algorithm and carry out the parallel design. Then we performed a detailed design of the algorithm implementation flow and analyzed the clock cycle. Finally, the correctness of our proposed parallel algorithm is verified on SD11. Compared with the serial AFS and parallel AFS algorithms based on electronic computers, the TOC-based AFS algorithm (TOC-PAFS) proposed in this paper effectively reduces the search time and improves the search performance of complex multi-peaked function optimization problems.

Automated summary

Researched and implemented a TOC-based artificial fish swarm algorithm that improves the search performance of complex multi-peaked function optimization problems through parallel design and utilization of high-performance processor bits.