Locality sensitive hashing-aware fruit fly optimization algorithm and its application in edge server placement

Abstract

Abstract As is well known that the global optimization ability of the Fruit fly Optimization Algorithm (FOA)is weak because it is easy to fall into local optimum. In this paper, a Fruit Fly Optimization Algorithm based on Locality Sensitive Hashing-aware (LSHFOA)was proposed. The locality sensitive hashing mechanism to optimize the generation mechanism for swarm population individuals was used, which can improve the individual diversity of the population. Meanwhile, when the fruit fly population falls into the local optimum, the locality sensitive hashing mechanism was adopted to change the population location, which is used for jumping out of local optimal limits. To verify the performance of LSHFOA, it was compared with FOA and its improvement algorithms CFOA, and IFFO with 8 representative benchmark functions. A large number of experimental results showed that LSHFOA has a faster convergence speed and higher precision of optimization for function optimization, especially in high-dimensional multi-peak functions. In addition to the theoretical evaluation, we also evaluate its performance in a real-world scenario. Generally, an edge computing environment, as an extension of cloud computing, can allow the users to access the network in a low-latency manner. In this way, to capture the high-speed convergence advantage, this paper makes the first attempt to tackle a classic research problem in the edge computing environment, i.e., the edge server placement problem. The experimental results show that the new algorithm has an excellent application effect.

Keywords

• Fruit fly optimization algorithm
• Locality sensitive hashing
• Swarm intelligence algorithm
• Global optimization capacity
• Edge server placement problem
• Edge computing