Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing

Abstract

Advancements in the Industrial Internet of Things (IIoT) have yielded massive volumes of data, taxing the capabilities of cloud computing infrastructure. Allocating limited computing resources to numerous incoming requests is crucial for cloud computing and referred to as a task-scheduling-in-cloud-computing (TSCC) problem. In order to ameliorate the performance of a particle swarm optimizer (PSO) and broaden its application to TSCC, this article introduces an opposition-based simulated annealing particle swarm optimizer (OSAPSO) to address PSO's premature convergence issue, particularly when tackling high-dimensional complex problems like TSCC. OSAPSO is a novel combination of opposition-based learning (OBL), evolution strategy, simulated annealing (SA), and swarm intelligence. At its initial stage, a swarm is formed at random by using OBL to guarantee its diversity with a light computational burden. A multiway tournament selection approach is then utilized to pick parents to produce a new offspring swarm by using two novel evolutionary operators, namely, damping-based mutation and inversion-scrambling-based crossover. OSAPSO is given a powerful exploration capacity by adopting the survivor probabilistic selection of SA, which accepts subpar solutions with a certain probability. Finally, PSO itself kicks in, making a good tradeoff between solution diversity and convergence speed of OSAPSO. Due to the nonconvex discontinuous nature of TSCC, OSAPSO is modified to clone it into a discrete optimization problem. Within a heterogeneous cloud computing environment, OSAPSO and eight well-regarded competitors are examined on a set of multiscale IIoT heterogeneous task groups. In terms of power consumption, monetary cost, service makespan, and system throughput, experimental results reveal that OSAPSO beats its peers in IIoT task scheduling of cloud systems.