Previously, several research studies have been conducted. Various types of energy consumption resources, their consumption, and different parameters worth consideration for the occupants have been explored by researchers. For example, the authors in [19
] introduced an equation-based system for energy efficiency management, intelligent buildings modeling, simulation, and power consumption optimization in smart buildings. These equation-based systems are used for declaring the relationship among various variables that play roles in any form in energy-efficient buildings. These systems make use of computational algebra for enabling simpler architecture of the buildings for generating efficient and effective codes in order to perform simulations and the process of optimization. The authors applied the idea to different microgrids, a few buildings, some controllers used for controlling the temperature of the buildings, interactive inverters for maintaining the power quality, and HVAC systems. The systems were found to be efficient in obtaining the desired goals. Similarly, a multiagent control system based on information fusion has been proposed by the authors in [20
]. In order to control and manage the energy inside the buildings, they used to order weighted aggregated averaging aggregation. The proposed approach maximizes user comfort and minimizes power consumption. There are many internal and external factors influencing user comfort inside the buildings. All these factors have some strong relationship to affect the occupants of the building. A model was proposed by the authors in [21
] for understanding this relationship to keep them under consideration when developing some energy and user comfort-related approach for residential buildings. Both the indoor and outdoor environmental factors have been considered by the authors in [22
] for managing the user comfort and power usage management system. For efficient energy management systems, different types of classification, prediction, and optimization models and techniques have been presented in the literature. The authors in [23
] developed an energy management system for providing the user comfort in the buildings connected to microgrids. The model was developed for balancing the consumption and production of the energy by different power-generating resources. The developed system takes into account the amount of energy generated by various power-generating sources, providing it to the buildings and, at the same time, maintaining user’ comfort, according to the requirements. Similarly, an efficient algorithm was proposed by the authors in [24
] for management of the demand response and thermal comfort optimization in an environment facilitated with renewable power sources and storage devices. The authors targeted two major issues present in the literature, focusing on energy consumption and occupant satisfaction. The first issue resolved by the authors was the integration of power generation and consumption with the user behavior and guaranteeing the user thermal comfort. The second issue targeted was to develop a robust and scalable demand response system. A multi-objective simulation-based multi-objective optimization problem was solved by the authors in [25
] by proposing a model based on the particles swarm optimization algorithm. For enhancing the performance of a building energy management system, the authors applied a single-objective and multi-objective particle swarm optimization (PSO) and coupled it with EnergyPlus simulation software. In order to evaluate the performance, effectiveness, and capability of the proposed model, it was applied to single-room architecture in which various types of building management parameters were considered that can affect the energy consumption, and four major climate areas in Iran were selected for testing. In the optimization process of the approach, multi-criterion and single criterion optimization analyses of yearly based cooling/heating systems and lighting system power consumptions are tested for understanding the relationship between the annual energy consumption minimization and the objective function. In the same manner, a simulation-based system, in combination with the genetic algorithm, was used by [26
] for a multi-objective optimization process in which the benefit of power consumption was maximized and the thermal discomfort was minimized. In a similar attempt, the genetic algorithm was applied by the authors in [27
] for a multi-objective optimization problem in which the energy consumption was minimized and the user thermal comfort was maximized. In order to improve the prediction power of the neural network, the authors also applied the GA, and then, the thermal comfort and the energy consumption were predicted. Lastly, the authors established a multi-objective building using the GA and neural network for the optimization process. Similarly, single-objective and multi-objective optimization models were developed by the authors in [28
] using the nondominated sorting genetic algorithm. The inputs of the system were different environmental parameters, including weather data, lighting system, renewable energy management parameters, cooling/heating load, and the occupancy schedules. The output of the optimization process is the optimal energy system design and the user-preferred environment. In a similar fashion, the nondominated sorting genetic algorithm was used by the authors in [29
] for minimizing the visual and thermal discomforts. Different types of building parameters were considered in the proposed work, e.g., the locations of the buildings, geometry of the buildings, volume of buildings, number of rooms, and types of rooms. An approach was developed by authors in [30
], focusing on energy efficiency and the management of smart or energy-efficient buildings. All of the energy management systems were divided into four categories—namely, building space, lighting system, HVAC systems, and occupancy and comfort management systems. Similarly, the authors in [31
] used deep reinforcement learning for the energy optimization in buildings. Different types of energy resources and consumption appliances were considered in the work.
Shimin Li et al. [32
] proposed another stochastic optimization approach known as the slime mold algorithm (SMA) in light of the oscillation method of slime mold in natures. In that particular research work, they introduced few novel features, utilizing a remarkable numerical model dependent on adaptive weights so as to mimic the way toward creating positive and negative inputs of sludge form waves during spreading. The incitement has been controlled with the assistance of a bio-oscillator for producing the ideal way for associating food with a great exploratory capacity and exploitation affinity. They led broad, similar trials of proposed SMA with a few metaheuristic approaches utilizing a lot of benchmark functions to confirm its productivity. Furthermore, they utilized four smart building problems to approve the adequacy of the algorithm in severe optimizing problems. The conclusive outcomes introduced in various tables and figures demonstrated that the proposed SMA is a new enhancement procedure on various search landscapes. In [33
], the authors presented another improved algorithm, named the equilibrium optimizer (EO). The EO is propelled by the control volume mass equalization models, which were utilized to evaluate both the dynamic and balanced states in material science. In their proposed EO approach, every particle is considered as a single solution, and its position is referred to the concentration of these particles while acting as a search agent. The positions are randomly updated for the current-best solutions by the search operators and named equilibrium candidates. At the point when the last best particle from the equilibrium candidates reaches the balanced state, it is then considered as the ideal output. An unmistakable “generation rate” term is demonstrated to help the exploration and exploitation capacity of the EO for the evasion of nearby minima. The authors in [34
] introduced Henry Gas Solubility Optimization, a novel material science-based approach that imitates the conduct of Henry’s law. This work is being roused by the Henry’s law of dissolvability, which demonstrates that “on a steady temperature, the particular measure of a gas which dissolvable in a particular volume and sort of fluid is directly proportional to the partial pressure of that gas in equilibrium with that fluid”. The exploratory outcomes led from 47 optimization problems and the CEC’17 test suite with three engineering design problems uncovered that Henry Gas Solubility Optimization (HGSO) focuses on a harmony between exploration and exploitation capacities of the search space, which avoids the problem of local optima.
There are a few major drawbacks associated with all the previously proposed optimization algorithms applied for the energy optimization and management in residential buildings. Firstly, most of them are used in their standard operational working mechanisms. This independent working characteristic of most of the optimization models makes them weak in handling the parameters from the environment, as well as from the user, leading to an inefficient energy management and optimization process. Secondly, the optimization algorithms are suffering from an imbalanced relationship between the exploration and exploitation capabilities of the solution search space that leads to their failure in getting the most optimal solution of an optimization problem. Due to this property, most of the proposed approaches for the energy optimization process have not been successful in achieving minimum power consumption and maximum user comfort. Lastly, some optimization algorithms have been used in their enhanced, modified, or in hybridization with other optimization algorithms; their complexities increase adequately due to their sophisticated operations in the introduction of modifications or embedding other algorithms’ characteristics. In order to overcome all of these drawbacks, a new, simple, and efficient model is proposed in this work. The newly developed model is a hybrid of two optimization algorithms—namely, the FA and GA. The hybridization process makes the model more powerful in minimization of the power consumption and maximization of the user comfort as compared to the independent and conventional optimization techniques. A balanced relationship between the exploration and exploitation capabilities of the solution search space of the standard FA has been achieved by embedding rich operators of the GA used for exploration and exploitation, making it powerful in order to get the minimum power consumption and maximum user comfort. Lastly, the proposed model is easy to implement, because both the FA and GA are easy in terms of integration, running, development, and parameter tuning. The detailed description, working procedure, components, and complete structure of the proposed approach are given in various sections of this paper.