Global Optimization Algorithm for Cloud Service Composition

Hongwei YANG  Fucheng XUE  Dan LIU  Li LI  Jiahui FENG  

IEICE TRANSACTIONS on Information and Systems   Vol.E104-D   No.10   pp.1580-1591
Publication Date: 2021/10/01
Publicized: 2021/06/30
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDP7233
Type of Manuscript: PAPER
Category: Computer System
web service composition,  beetle antennae search algorithm,  particle swarm optimization algorithm,  global optimization algorithm,  

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Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.