Modified SSA for Solving Multiprocessor Scheduling Problems

Task Scheduling is the way of processing, optimizing and controlling the processing in a process. It can be applied in different applications, like manufactory industries, aerodrome, and data computation process. Multiprocessor task scheduling issue can be expressed as finding a time table for an overall job diagram to execute on a multiprocessor framework so the timetable length can be limited. Additionally, there are some problem existences in multiprocessor scheduling such as larger waiting time, a lower performance due to indefinite halting of a process or task. This paper presents a productive technique to address the Multiprocessor Machine Scheduling Problems (MMSPs) while utilizing an adjusted salp swarm algorithm (SSA). The new strategy, called MSSA or modified SSA, utilizes the salp swarm algorithm (SSA) as a local search for Harris Hawks Optimization (HHO) to upgrade its performance and to diminish its makespan. In the test case, the author experimented with the MSSA with real-time task scheduling invariants of the number of processors. The MSSA shows better execution in both little and huge issue cases.