Hybrid collaborative multi-objective fruit fly optimization algorithm for scheduling workflow in cloud environment

Abstract

Scheduling the complex workflows in cloud environment have drawn enormous attentions because the distinct features of the cloud resources. Most of the previous approaches ignored the multiple conflicting objectives of workflow scheduling and resources provisioning. In this paper, a novel hybrid collaborative multi-objective fruit fly optimization algorithm (HCMFOA) is developed to optimize both the execution time and cost. In the proposed HCMFOA, a reference points-based cluster strategy is introduced to dynamic divide the swarm into multiple sub-swarms. Moreover, a hybrid initial strategy is designed based on non-linear weight vector and two assignment rules of tasks to initialize the location of all the fruit flies in the problem space. In the collaborative smell-based foraging, three effective problem-specific neighborhood operators are employed to collaborative explore the global scope. In multi-objective vision-based foraging, the sub-swarms based crossover operator is designed to perform exploitation in local region. Finally, an extensive computational experiment is conducted to validate the performance of HCMFOA. The statistical results reveal that HCMFOA significantly outperforms the existing state-of-the-art approaches.