An effective fruit fly optimization algorithm with hybrid information exchange and its applications

Abstract

As a newly proposed algorithm, fruit fly optimization algorithm (FOA) has been shown to have a strong capacity for solving numerical optimization problems. However, the basic FOA is faced with the challenges of poor diversity of the swarm and weak local search ability because of the improper osphresis operation and vision operation. To overcome these limitations synthetically, we propose an improved FOA based on hybrid location information exchange mechanism (HFOA) aiming at improving the swarm diversity in a more efficient way and well balance the global search and local search abilities. First, the proposed HFOA enables flies to communicate with each other and conduct local search in a swarm based approach. Second, osphresis operation is conducted in probability to balance the global search and local search processes. Finally, a mutation strategy called cataclysm policy is designed to help the flies jump out of the local extreme points. 18 complex continuous benchmark functions are used to test the performance of HFOA. Numerical experiments results indicate that HFOA outperforms main state-of-the-art algorithms. A classical non-deterministic polynomial hard problem—a widely-researched joint replenishment and delivery scheduling problem with resource restrictions is also used to further verify the ability of HFOA in solving practical operation management problems. Results show that HFOA can obtain lower operation cost than other widely used methods, demonstrating its ability to solve various complex optimization problems.