Abstract—The focus of this paper is on energy consumption optimization in smart homes (with/without RES) and increasing the user comfort level. The paper presents a functional and adaptable home energy management system with RES and an energy storage device for designing and implementing Demand Response (DR) programs. The four meta-heuristic techniques: Genetic Algorithm (GA), Wind-Driven Optimization (WDO), Grey Wolf Optimization (GWO) and Salp Swarm Optimization (SSA), are used to optimize the energy consumption cost for a home energy environment. In the process of identifying and proposing a dedicated home energy optimization algorithm, this paper investigated four optimization algorithms with four different pricing schemes: Time of Use (TOU) pricing, Real-Time Pricing (RTP), Critical Peak Pricing (CPP), and Day-Ahead Pricing (DAP) schemes. The results obtained using these pricing schemes are validated and compared in a common smart home environment. Further, the results show that by integrating Renewable Energy Sources (RES) and a battery reduces the electricity bill by 10.89% (without RES) and 38.88% (with RES), as well as the peak-to-average ratio (PAR) by 59.97% (without RES) and 64.98% (with RES) when compared to the energy consumption cost obtained without-scheduling technique. Moreover, without RES, the SSA algorithm based home energy management system outperforms the other algorithms particularly with the TOU pricing scheme.