Analysis of the Distributed Immune Inspection Equipment Sensor Scheduling Model Based on Adaptive Dynamic Probabilistic Particle Swarm Optimization. (16th December 2021)

• Record Type: Journal Article
• Authors: Zhou, Yi; Xia, Weili; Peng, Shengping
• Other Names: Shi Guolong Academic Editor.
• Is Part Of: Journal of sensors. Volume 2021(2021)
• Publication Date: 2021-12-16
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2021/6598782
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Based on the analysis of bacterial parasitic behavior and biological immune mechanism, this paper puts forward the basic idea and implementation method of an embedding adaptive dynamic probabilistic parasitic immune mechanism into a particle swarm optimization algorithm and constructs particle swarm optimization based on an adaptive dynamic probabilistic parasitic immune mechanism algorithm. The specific idea is to use the elite learning mechanism for the parasitic group with a strong parasitic ability to improve the ability of the algorithm to jump out of the local extreme value, and the host will generate acquired immunity against the parasitic behavior of the parasitic group to enhance the diversity of the host population's particles. Parasitic behavior occurs when the number of times reaches a predetermined algebra. In this paper, an example simulation is carried out for the prescheduling and dynamic scheduling of immune inspection. The effectiveness of prescheduling for immune inspection is verified, and the rules constructed by the adaptive dynamic probability particle swarm algorithm and seven commonly used scheduling rules are tested on two common dynamic events of emergency task insertion and subdistributed immune inspection equipment failure. In contrast, the experimental data was analyzed. From the analysis of experimental results, under the indicator of minimum completion time, the overall performance of the adaptive dynamic probability particle swarm optimization algorithm in 20 emergency task insertion instances and 20 subdistributed immune inspection equipment failure instances is better than that of seven scheduling rules. Therefore, in the two dynamic events of emergency task insertion and subdistributed immune inspection equipment failure, the adaptive dynamic probabilistic particle swarm algorithm proposed in this paper can construct effective scheduling rules for the rescheduling of the system when dynamic events occur and the constructed scheduling. The performance of the rules is better than that of the commonly used scheduling rules. Among the commonly used scheduling rules, the performance of the FIFO scheduling rules is also better. In general, the immune inspection scheduling multiagent system in this paper can complete the prescheduling of immune inspection and process dynamic events of the inspection process and realize the prereactive scheduling of the immune inspection process.