  Cloud computing distributes computing tasks across numerous distributed
resources for large-scale calculation. The task scheduling problem is a
long-standing problem in cloud-computing services with the purpose of
determining the quality, availability, reliability, and ability of the cloud
computing. This paper is an extension and a correction to our previous
conference paper entitled Multi Objective Scheduling in Cloud Computing Using
MOSSO published in 2018 IEEE Congress on Evolutionary Computation. More new
algorithms, testing, and comparisons have been implemented to solve the
bi-objective time-constrained task scheduling problem in a more efficient
manner. Furthermore, this paper developed a new SSO-based algorithm called the
bi-objective simplified swarm optimization to fix the error in previous
SSO-based algorithm to address the task-scheduling problem. From the results
obtained from the new experiments conducted, the proposed BSSO outperforms
existing famous algorithms, e.g., NSGA-II, MOPSO, and MOSSO in the convergence,
diversity, number of obtained temporary nondominated solutions, and the number
of obtained real nondominated solutions. The results propound that the proposed
BSSO can successfully achieve the aim of this work.
