An Improvement of AFSA in Global Search with Scout Swarms

Abstract

Since the emergence of AFSA (Artificial Fish Swarm Algorithm), because of a lot of good features in solving complex optimization problems, AFSA has been studied by scholars from domestic and overseas, however it also has some shortcomings such as high time complexity, lack of balance between global search and local search. In recent years lots of researchers have attempted to improve this algorithm. In this paper, a new improvement will be introduced to solve its ability of balance between global search and local search, and its name is Scout-AFSA (Scout-Artificial Fish Swarm Algorithm). And the final experiment results show that Scout- AFSA does work better than AFSA in global search. Index Terms - AFSI (Artificial Fish Swarm Intelligence), Scout- AFSA, Global Search. I. Introduction At first, we should have an entire look at how nature fish swarm works, and it will help us have a better understanding of AFSA. In the nature world, almost all the things (living and non- living things) are going with the different performances but with the same law, maybe the only difference between them is the different visual opinion used by ourselves in our mind.