Virtual machine (VM) consolidation for cloud data centers is one of the hottest research topics in cloud computing. It is challenging to minimize the energy consumption while ensuring QoS of the hosts in cloud data centers, which is essentially an NP-hard multi-objective optimization problem. This study proposes an energy efficient hybrid swarm intelligence virtual machine consolidation method (HSI-VMC) for heterogeneous cloud environments to address this issue, which including peak efficiency based static threshold overloaded hosts detection strategy (PEBST), migration ratio based reallocate virtual machine selection strategy (MRB), target host selection strategy, hybrid discrete heuristic differential evolutionary particle swarm optimization virtual machine placement algorithm (HDH-DEPSO) and load average based underloaded hosts processing strategy (AVG). Specifically, the combination of PEBST, MRB, and AVG is able to detect the overloaded and underloaded hosts and selects appropriate virtual machines for migration to reduce SLAV and virtual machine migrations. Also, HDH-DEPSO combines the advantages of DE and PSO to search the best virtual machine placement solution, which can reduce cluster's real-time power effectively. A series of experiments based on real cloud environment datasets (PlanetLab, Mix, and Gan) show that HSI-VMC can reduce energy consumption sharply with accommodate to multiple QoS metrics, outperforms several existing mainstream energy-aware virtual machine consolidation approaches.