## 2 Related Works  
A multi-objective genetic algorithm is proposed for dynamic demand prediction to minimize energy consumption and increase resource utilization [11]. Many heuristic algorithms have been used in the VM placement problem: ant-colony-based VM placement is formulated as a multidimensional bin-packing problem to control resource wastage and energy consumption simultaneously [12]. Group technology-based cuckoo optimization technology is used to control the datacenter's operating cost, considering task migration, VM creation and energy consumption [13]. An improved practical swarm optimization is used to increase the quality of service with reduced power consumption [14]. A nature-inspired genetic algorithm and bacterial-foraging algorithm are used for efficient task scheduling to reduce energy consumption and to minimize the overall makespan of the task [15]. A modified genetic algorithm is proposed by generating an initial population with the Max-Min approach to get a better makespan [16]. A new heuristic embedded with the ant colony is proposed to reduce energy consumption [17]. The nature of the harmony search algorithm adopts itself to suit both discrete and continuous variable problems [18]. Virtual machine consolidation is performed using harmony memory search by reducing the number of active machines along with the quality of service requirement [19]. Ant-colony-based consolidation of VMs is utilized to minimize energy consumption with acceptable performance levels [20]. The modified practical swarm optimization approach is used to perform virtual machine consolidation with an aim to reduce power reduction using CloudSim [21]. Meta-heuristic approaches are a considerable alternate to obtaining a solution that offers a trade-off between computational cost and the best fitness solution for NP (non-deterministicpolynomial-time)-hard problems. There is no single global optimal algorithm that derives the best optimal solution in all optimization schemes [22]. A few optimization algorithms, like ant colony and genetic algorithms, follow the search path of recently generated partial solutions, few others provide a single-point search solution, in which the search path is decided based on the solution generated at each interval. Many meta-heuristic algorithms are population-based, storing a set of feasible solutions in the search path, which paves the way for efficient exploration in global search space. Metaheuristic algorithms have been identified as efficient tools to solve engineering design problems [23]. Harmony Search Algorithm (HSA) has been used in the majority of applications, like power and energy management, distributed generation and capacitor placement, transmission network planning, job scheduling, neural network training, water pump switching, water distribution network, heat and power economic dispatch, charge scheduling of energy storage systems with renewable power generators, estimation of life cycle cost and carbon dioxide equivalent emission of the buildings [24; 25; 26; 27; 28; 29; 30] and so on. A hybrid approach, coral reefs optimization has been used to tackle the feature selection problem for an extreme learning machine prediction model for short-term wind prediction in [31]. Optimization techniques are widely used in many sustainable energy sources such as building, environment and energy. The finding of much recent scientific research works highlights the growth and popularity of optimization approach towards the reduction in the CO\({}_{2}\), energy consumption and power cost. The potential and strength of meta-heuristic algorithms in exploration and exploitation have made its applicability in sustainable energy optimization problems possible, which is the current need [32]. The increasing demand for the power and environmental challenges of global warming has made renewable energy (solar, wind and hydro) an inevitable substitute of existing power sources to reduce greenhouse gas emissions.