A hybrid whale optimization algorithm with differential evolution optimization for multi-objective virtual machine scheduling in cloud computing

Virtual machine (VM) scheduling in a dynamic cloud environment is often bound with multiple quality of service parameters; therefore, it classed as an NP-hard optimization problem. Swarm-based metaheuristics, such the whale algorithm (WOA), have gained notable reputation for solving problems. The unique bubble-net hunting behaviour and fast convergence led to development hybrid multi-objective algorithm-based differential evolution (M-WODE) technique solve VM (DE) strategy used replace randomly generated solution produced by WOA ensure diversity strengthen local search M-WODE. In addition, DE applied Pareto front escape optima entrapment experimental results showed that proposed M-WODE outperformed previous algorithms most cases on makespan cost trade-off.