## 1 Introduction
It has been successfully applied for solving numerous real-world problems, such as protein structure prediction [20], segmentation of synthetic aperture radar images [21], pollution source identification [22], balancing loads and minimizing energy consumption in mobile sink wireless sensor networks [23], minimization of truss structure weight [24], and many others. In the meantime, there is room for improvement to enhance the ABC algorithm efficiency [19]. For example, the issue of premature convergence and balancing the exploration and exploitation of the ABC's search agents need to be addressed. Inertia weight is commonly used in metaheuristics for this purpose, especially for PSO [25]. A number of works have also been reported on the integration of inertia weight with ABC. Dynamic increasing inertia weight-ABC (DIW-ABC) is proposed in [26]. The inertia weight is multiplied by the previous food in the food update equation to control its influence on the generation of new food. There are two type of time dependent inertia weights proposed, exponentially increasing and linearly increasing. The findings show that the inertia weight changes and improves the convergence of the ABC. Meanwhile, in [27] the authors apply a linearly decreasing inertia weight. Similar to [26], the inertia weight is incorporated into the employed bee's food update equation, however the inertia weight is applied to the distance of the current food source with another food source. The larger inertia at the beginning of the search is claimed to encourage exploration, while the smaller inertia towards the end focuses on the fine tuning of the solution. The algorithm is found to have better results when compared to the original ABC and PSO. These promising findings on the successful applications of ABC to various real-world optimization problems and the improved performance of ABC with inertia weight motivates this work. The real-world problems tackled in the previous research are complex and are mostly nonlinear problems, similar to the problem considered here. Therefore, in this work, inertia weight-ABC (IW-ABC) is proposed for user comfort index maximization and energy consumption minimization. The comfort index is determined by three factors, namely the indoor temperature, air quality and illumination, while the energy consumption is based on the energy usage of the appliances used in controlling these factors. Three variants of the inertia weight, namely exponential, linearly increasing, and linearly decreasing are applied here. The IW-ABC search for the best temperature, air quality, and illumination and their efficiencies are tested using a dataset with 48 environmental temperatures, illuminations, and indoor air qualities. The findings show that statistically the proposed IW-ABC significantly improves the comfort index and lowers the energy consumption. The IW-ABC is also showing a better convergence behavior that allows more exploration and overcomes the premature convergence of the original ABC. This contributes to a better performance. The IW-ABC with linear increasing inertia has the best improved convergence. In the next section, existing works using various swarm intelligence algorithms are reviewed. The proposed solution is introduced in Section 3. The experimental setting is discussed in Section 4. In Section 5, the results and findings are presented and analyzed before the paper is concluded in Section 6.