## 1 Introduction  
The development strategies of human life provide a high level of urbanization that requires the inclusion of many new devices and applications [1]. These requirements draw a new energy demand request against the available generation capacity. Adding new generation capacity requires building new generation plants to support the required rising demand. However, building new traditional power generation plants leads to an increase in the greenhouse gasses (GHGs) that cause climate warming [2]. Renewable energy generation is one of the effective solutions for supporting high energy demand. However, high initial cost and intermittent supply are the drawbacks of depending on these sources [3]. Therefore, demand response (DR) is the compromising solution, which manages the available generation to support the required demand without adding new generation plants. DR works to manage the peak time energy demand (which is about 5% from the whole day times) and represents the high level of energy demand during the day. The development is basically relying on two factors, which are demand response pricing schemes and information and communication technologies (ICTs) that are based on the recent internet of things context. Accordingly, this provides the essential incentives to convert the current traditional power grid into a smart grid [4; 5]. A smart grid with its effective networks and applications makes the end-users the key factor for managing the load profile (load scheduling (LS)). This in turn lowers the energy bill in reflection of the decrease in the peak load demand. LS helps to defer the flexible user peak load from the peak time with a high price rate to the off-peak time with a lower price rate. However, deferring the peak load to the off-peak leads to generating a new peak time slot that must increase the price rate. Accordingly, load synchronization or load smoothing is applied. In turn, this supports end-user load scheduling without making a new peak load. Therefore, cooperative load scheduling is the key factor of load management that makes many end-users work together to schedule the load for benefiting both users and the utility company by lowering the energy bill and peak load.  
The available studies for load scheduling deal with cost and energy saving. However, these studies have the following issues: First, most studies have covered only one user of limited appliances [6; 7; 8; 9; 10; 11; 12]. These approaches may contribute to load and energy saving by using load scheduling but did not consider the load synchronization to prevent the generation of new peak load during load shifting. Second, combing many objectives is mostly converted into a single objective using the aggregated weighted-sum model [6; 7; 8; 9; 10; 11; 13; 14; 15; 16]. Third, multi-criteria decision making has not been widely utilized in sorting the given Pareto front solutions [6; 7; 8; 9; 10; 11; 13; 14; 15; 16; 17].  
Based on the previous research difficulties, the contributions of this work regarding load scheduling (LS) are listed as follows:  
1. First is a triple-objective LS, which utilizes dominant rank and is based on the artificial hummingbird algorithm (AHA) to realize end-user inconvenience, peak demand, and optimal cost. The given objectives are combined using multi-objective AHA (MAHA) to produce all sets of solutions with the considered criteria.
2. A hybrid multi-criteria decision making technique is considered to select the dominance solutions. This method is based on the removal effect of criteria (MEREC) and used for deriving appropriate weights for various criteria. After that, the VIKOR method is applied to find the best solution of load scheduling among a set of solutions produced by AHA.
3. Synchronized multi-user cooperative load scheduling (SMUCLS) is covered to overcome the issue of generating peak time caused by shifting user load away for minimum energy cost.
4. ToU and the adaptive consumption level pricing scheme (ACLPS) are applied to validate the proposed system against different price rates.