## 1 Introduction  
The delivery of services and resources on the Internet, which is packaged as Virtual Machine (VM), is called Cloud Computing (CC). The cloud makes facilities available for use by industries, companies, and organizations on a payment basis, depending on the magnitude of the utilization. This saves much of the cost for enterprises as it allows them to concentrate on the core mandate without building the cloud infrastructure on the organizations' premises. The use of CC as a platform for providing services has increased tremendously and been successfully utilized in recent years. The services provided by the CC platform are in different categories of cloud data center levels, such as Software as a service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) [1,2]. Generally, Cloud providers offer a heterogeneous set of resources to users with different performances and capacities. Hence, resource management must be formulated as an optimization problem that intends to optimize the allocation and utilization of cloud data center resources for users with different levels of capacity and usage. However, most of the existing optimization algorithms suffer from local optima entrapment and non-optimal resource consolidation, which makes them inefficient in managing data center resources [3, 4]. Local optima are defined as the relatively best solutions within a neighbor solution set that is not necessarily optimum. Therefore, local optima entrapment could result to slow convergence and non-optimal resource consolidation. The energy consumption of under-utilized Physical Machines (PM) in cloud data centers accounts for a substantial amount of the actual energy used [5, 6]. This can be attributed to the entrapment at the local optima resulting in an inefficient resource allocation policy of the data center [7, 8]. Simultaneously maintaining energy efficiency and the user's Service Level Agreement (SLA) is sacrosanct for the service providers [9, 10]. The cloud service providers require energy-saving strategies such as the VMs consolidation [11]. According to [12, 13], one of the main reasons for the resource inefficiency in current data centers is overprovisioning. It leads to inefficient utilization of resources hence, causing poor management of resources that depletes more energy and violation of SLA. For these reasons, energy efficiency and SLA compliance are becoming increasingly important for cloud data centers. The underutilization of Physical Machines (PMs) has also been attributed to the inefficient resource allocation policy of the data center [14, 15, 16]. However, central processing units (CPUs) are the most used resources, which fully depends on the PM configuration and the data center set up. CPUs are considered the single largest energy consumer within the Cloud data center. Therefore, it is often assumed that inefficient CPU utilization leads to an underutilized PM of the data center. However, all components are to meet the expected results in reality. Although the CPU is apparently underutilized, other components are already working at their limit. The data center's maintenance is expensive and endangers both humans and the natural environment by producing unwanted substances such as carbon monoxide [17, 18, 19]. However, resource management in large-scale data centers is a complex and challenging issue. The demand for large servers, storage, and network infrastructure to accommodate faster processing contributes to the energy consumption at the Cloud data center. Several multi-objective and optimization algorithms for efficient resource allocation have been suggested for minimizing energy consumption and violation of SLA at the data centers. The existing nature-inspired resource allocation algorithm in Cloud may not fully curtail the problem of energy consumption, thus, creating the need for an ideal solution.