You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Home Energy Management 
Chapter Title:  2) HEURISTICS AND METAHEURISTICS 
1. Abstract of Cited Paper (BIB001):  This paper deals with the problem of designing an efficient metaheuristics optimization method to tune the parameters of a Home Automation System (HAS). Using a simulation environment, the performances of a home automation system in exploiting limited resources (electricity, gas, hot water) can be described in terms of suitable indices that depend on behavioural parameters of the individual appliances and can be practically evaluated. The choice of the best set of parameters for the model is the common intention of most simulation studies and for this reason in recent years the field of the simulation optimization has received increasing attention. Our method has proven effective in finding good parameter configurations, even starting from very poor initial solutions. 
2. Abstract of Cited Paper (BIB002):  We use the Multi Agent System paradigm to model and analyse Home Automation System performance in exploiting limited resources such as electricity and hot water. In this paper we evaluate several approaches to the optimisation of Home Automation System performance using Tabu Search, and Single and Multi-objective Genetic Algorithms. The results show that the Genetic Algorithms achieve faster convergence than Tabu Search. Multi-objective Genetic Algorithm provides a diverse set of solutions for the decision maker. 
3. Abstract of Cited Paper (BIB003):  This paper describes a methodology for making robust day-ahead operational schedules for controllable residential distributed energy resources (DER) using a novel energy service decision support tool. The tool is based on the consumers deriving benefit from energy services and not on electric energy. It maximizes consumer net benefit by scheduling the operation of DER. The robust schedule is derived using a stochastic programming approach formulated for the DER scheduler: the objective function describing the consumer net benefit is maximized over a set of scenarios that model the range of uncertainty. The optimal scenario set is derived using heuristic scenario reduction techniques. Robust operational schedules are formulated for a ‘smart’ home case study with four controllable DER under stochastic energy service demand, availability of storage DER, and status of dynamic peak pricing. The robust schedule results in a lower expected cost but at the expense of long computation times. The computation period however is not much of a disadvantage because schedules are computed off-line. The consumer can prepare several DER schedules and simply choose the one to implement according to their perception of the coming day. The robust schedules are formulated using an improved version of co-evolutionary particle swarm optimization. 
4. Abstract of Cited Paper (BIB004):  Particle Swarm Optimization (PSO) is a promising evolutionary algorithm, which has been used in a wide range of applications, due to its simple implementation, fast convergence, parallel behavior, and versatility in working with continuous and discrete domains. In this paper, we consider its application to the load balancing problem, in green smart homes. Specifically, an adapted version of the Binary PSO has been used to determine the optimal distribution of energy resources, across different green energy sources in a green smart home. The case study of interest considers the usage of solar and wind energy, as green energy sources for the green smart home. Results demonstrate the effectiveness of the algorithm, in terms of the optimal outcome (efficient distribution of energy resources), finding installation material surplus, and the execution speed of the algorithm. 
5. Abstract of Cited Paper (BIB005):  Buildings all around the world consume a significant amount of energy, which is more or less one-third of the total primary energy resources. This has raised concerns over energy supplies, rapid energy resource depletion, rising building service demands, improved comfort life styles along with the increased time spent in buildings; consequently, this has shown a rising energy demand in the near future. However, contemporary buildings’ energy efficiency has been fast tracked solution to cope/limit the rising energy demand of this sector. Building energy efficiency has turned out to be a multi-faceted problem, when provided with the limitation for the satisfaction of the indoor comfort index. However, the comfort level for occupants and their behavior have a significant effect on the energy consumption pattern. It is generally perceived that energy unaware activities can also add one-third to the building’s energy performance. Researchers and investigators have been working with this issue for over a decade; yet it remains a challenge. This review paper presents a comprehensive and significant research conducted on state-of-the-art intelligent control systems for energy and comfort management in smart energy buildings (SEB’s). It also aims at providing a building research community for better understanding and up-to-date knowledge for energy and comfort related trends and future directions. The main table summarizes 121 works closely related to the mentioned issue. Key areas focused on include comfort parameters, control systems, intelligent computational methods, simulation tools, occupants’ behavior and preferences, building types, supply source considerations and countries research interest in this sector. Trends for future developments and existing research in this area have been broadly studied and depicted in a graphical layout. In addition, prospective future advancements and gaps have also been discussed comprehensively. 
6. Abstract of Cited Paper (BIB006):  The present study uses the DHW (domestic hot water) electric backup from solar thermal systems to optimize the total electricity dispatch of an isolated mini-grid. The proposed approach estimates the hourly DHW load, and proposes and simulates different DR (demand response) strategies, from the supply side, to minimize the dispatch costs of an energy system. 
7. Abstract of Cited Paper (BIB007):  Abstract Utility demand response (DR) programs and time-of-use (TOU) tariffs are designed to reduce customers loads as well as energy costs during peak periods in commercial and residential buildings. While TOU tariff is structured based on dividing a day into several periods with different corresponding electricity prices, the concept can be expanded for developing day-of-use (D-TOU) tariff, where different days in a week are treated differently, as experienced for industrial loads. Further, the advantages of such tariffs can be realized when demand side management technologies such as thermal energy storage (TES) in conjunction with heat pump systems are utilized. The goal of this study is to evaluate the effects of D-TOU tariff with four day types in a week on DR programs operation for an industrial customer with TES charged by an electric heat pump. The four day types considered in a week include Monday as start-up working day, Tuesday to Friday as regular working days, Saturday as half-day working day, and Sunday as weekend day. To achieve the goal, TOU and D-TOU tariffs are modeled and the TES system equipment capacities and operation with cooling and heating tanks are optimized based on particle swarm optimization for an industrial building load. The results for various pricing scenarios show that D-TOU tariff can be beneficial to the utility company and customer, as peak loads for electricity in day types 1, 2, 3 and 4 are reduced by 54, 52, 47 and 44% and customer costs for electricity cooling are lowered by 14, 13, 11 and 8%. 
8. Abstract of Cited Paper (BIB008):  Abstract This work studies the problem of appliances scheduling in a residential unit. An appliance-scheduling model for the home energy management system (HEMS) is established based on day-ahead electricity prices and photovoltaic (PV) generation. The HEMS receives the meter data and calculates the scheduling strategies, then the HEMS sends control signals to achieve the on/off control of the appliances through the ZigBee (a wireless communication technology with low power consumption in short distance). The study starts with a view to minimizing the summation of the electricity payments, the consumer's dissatisfaction (DS), and the carbon dioxide emissions (CDE), and the constraints specify the restrictions on the operating time and the power consumption of the appliances. A cooperative multi-swarm particle swarm optimization (PSO) algorithm is adopted to solve the combinational optimization problem. The appliances can be categorized into shiftable and non-shiftable appliances. For the shiftable appliances, the start time and power of the appliances can be scheduled flexibly in the case of the announced electricity prices. Furthermore, the plug-in hybrid electric vehicle (PHEV) is introduced to charge or discharge for energy management. Specially, the ability of selling electricity (SE) to the power grid is studied for appliances scheduling. Finally, the simulation results demonstrate that the cooperative multi-swarm PSO algorithm shows good convergence performance under different scenarios. Moreover, The electricity payments can be reduced by considering the carbon dioxide emissions in the objective function and selling electricity to the power grid, which also achieves the peak load curtailment. 
9. Abstract of Cited Paper (BIB009):  Abstract This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other well-performing evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms' ability to optimize a multi-objective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio.

Literature Review:
For large problems, mathematical optimisation methods are computationally expensive, being heuristics and metaheuristics approaches a valuable alternative. They rely on high-level procedures to search for admissible solutions, resulting in a lower computational burden than mathematical optimisation methodologies. They are particularly attractive for problems where it is typically easier to find one suboptimal solution, but extremely difficult and time-consuming to find a global solution. In this class of methods, genetic algorithms (GA) BIB007 -  ,  , BIB006 and differential evolution (DE) BIB010 have stood out in the context of HEMSs, along with swarm intelligence-based algorithms, namely particle swarm optimisation (PSO) BIB009 , BIB004 , BIB005 , BIB008 and tabu search BIB002 , BIB003 , BIB001 .