Comparative Analysis of Swarm Intelligence Optimization Techniques for Cloud Scheduling
Swarm intelligence is the property that deals with collective behavior of decentralized and self-organized systems. For algorithm with complex problems, swarm intelligence is one among the successful paradigms. This paper is to analyze and compare most successful optimization techniques inspired by swarm intelligence: ant colony optimization and particle swarm optimization. In cloud computing, scheduling can be more effective by achieving lower makespan time. An elaborative comparison is carried out to investigate the performance of ant colony optimization and particle swarm optimization techniques.