4.2.4. Scheduling

Parashar et al. [141] used modified elephant herding optimization (MEHO) to model uncertain renewable generation. The analysis of the computational results indicated that the proposed MEHO approach had significant e ffects on the operational managemen<sup>t</sup> of the microgrid compared with the deterministic approach.

Cahig et al. [142] proposed a decision tool based on EHO for a virtual power plant (VPP) scheduling problem. The algorithm was illustrated for a test system with a VPP. The results showed that the canonical variant of EHO yielded the optimal scheduling, which suggested that it performed well as a decision support tool to the VPP operator.

Sarwar et al. [143] used EHO to solve a home energy managemen<sup>t</sup> system (HEMS) scheduling problem. Simulations of a single home with 12 appliances were performed and the results showed the EHO technique performed better than the other reported algorithms in reducing the waiting time and cost.

Parvez et al. [144] used two optimizing techniques, EHO [105] and harmony search algorithm (HSA) [99], to evaluate the performance of a home energy managemen<sup>t</sup> system (HEMS). The simulation results revealed that the proposed method was more e ffective in terms of electricity cost.

Mohsin et al. [145] implemented the EHO technique to solve the scheduling of smart home appliances. The simulation results revealed that EHO performed much better in terms of total cost and peak load reduction for di fferent operation time intervals (OTIs). In addition, EHO with shorter OTIs provided better results compared with longer OTIs.

Gholami et al. [146] developed improved EHO to solve large instances for hybrid flow shop scheduling problems. The performance of the proposed algorithm was compared with two available algorithms, which were SA and shu ffled frog-leaping algorithm (SFLA). Based on the results, the developed approach outperformed the other algorithms.

Fatima et al. [147] developed an efficient optimization method via the hybridization of two optimization algorithms, namely EHO [105] and the FA [69]. This method was used to reduce the electricity cost for home energy managemen<sup>t</sup> controller problems. The results indicated that the proposed hybrid optimization technique performed more efficiently for achieving the lowest cost and maximizing consumer satisfaction.

#### 4.2.5. Electrostatic Powder Coating Process

Luangpaiboon et al. [148] proposed a modified simplex EHO algorithm with multiple performance measures (MEHO). MEHO was used to solve the optimization of electrostatic powder coating process parameter optimization problems. According to some performance measures, two phases based on the response surface methodology were applied to study the EHO parameter levels. The simulation experimental results demonstrated that MEHO was more efficient compared with the previous operating condition.

#### 4.2.6. Image Safety Model

Shankar et al. [149] proposed an image safety model based on the EHO algorithm. Two keys, a general public key and a non-public key, were optimized by utilizing adaptive EHO (AEHO). The device was optimized by a hybrid algorithm applying encryption and optimization techniques which mixed the functionality of encryption and digital signatures. The experimental results indicated that the confidentiality of the image was ultimately upheld.

Chibani et al. [150] introduced EHO into the quality of service (QoS) aware web service composition. It was shown that the proposed method offered excellent performances compared with PSO in terms of convergence speed, scalability, and fitness evaluations.
