Cloud computing relies heavily on load balancing, which ensures that all of the resources, such as servers, network interfaces, hard drives (storage), and virtual machines (VMs), stored on physical servers, are working at full capacity at all times. A typical problem in the cloud is load balancing, which makes it difficult to keep the performance of the applications in line with the Quality of Service (QoS) measurement and the Service Level Agreement (SLA) contract that cloud providers are obligated to give to organizations. It's difficult for cloud providers to fairly divide the work between their servers. Multi-objective optimization (MOO) algorithms, ant colony optimization (ACO) algorithms, honey bee (HB) algorithms, and evolutionary algorithms are all examples of this type of method. The foraging activity of insects like ants and bees served as inspiration for the ACO and HB algorithms. The single-objective optimization problems can be solved by these two techniques, though. ACO and HB need revisions to work with MOPs. This paper summarizes the surveyed optimization methods and describes the modifications made to three specific algorithms.