M-PASOA: A Multi- orientation Mission Dispatching Strategy Based on Probabilistic Adaptive Seagull Optimization in Cloud Computing

Efficacious mission dispatching is a pivotal challenge of cloud computing. Finding the best solution to this NP-hard problem is challenging. To lessen fulfilling time, expenditure, and energy drain of cloud tasks, a cloud computing mission dispatching manoeuvre stemmed from probabilistic adaptive seagull optimization method M-PASOA is put forward, which improves the utilization of resources through the presented probabilistic adaptive seagull optimization (PASOA) algorithm. In PASOA, a good point set-based population initialization strategy is first presented to enhance the ergodicity of the initial population. Specifically, we give the levy flight strategy to dynamically adjust the moving situation of the supreme venue of population that enhances global ability of the algorithm search and optimization. Moreover, we present a probability adaptive location update strategy, which updates the population location via the probability through sine-fuch chaotic mapping, and then employs a random mutation strategy to combat it sinking into topical venue, thereby making seagull close to the global optimal position faster. Extensive simulations are performed to verify the performance of M-PASOA. Compared with the existing algorithms, our algorithm can effectively improve search accuracy and reduce accomplishing time, expenditure and energy drain.