# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 2 Review of Related Works
### Related Studies in Literature  
In [3], [PERSON] et al. proposed a smart _hill-climbing_ algorithm based on _RHC_ to configure the parameters in the server that can influence the server response automatically. They formulated the problem of finding the optimal configuration for a given application as the black-box optimization problem. They carried out extensive experiments with an online brokerage application running in a _WebSphere_ environment. The results demonstrated that the algorithm is superior to traditional heuristic methods. In [4], [PERSON] et al. proposed a hybrid annealing particle swarm optimization localization algorithm based on the _simulated annealing_. They proposed the minimum positioning error weighting model to reduce the non-line-of-sight error of anchor nodes during positioning. In experiments, they deployed 25 nodes on the square area of 100 m \(\times\) 100 m where the communication radius of a node is 20 m. The results showed that the localization average error of distance when locating these nodes by using the algorithm is 0.3775 m. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Algorithm**} & **Time** & **Number of** & **Deterministic** & **Local** & \\  & **Simplicity** & **Parameters** & **Initial Solution** & **Search** & **Hill Climbing** \\ \hline paraOpt & \(O(nc)\) & 4 & yes & yes & yes \\ \hline RHC & \(O(nc)\) & 3 & no & yes & no \\ \hline SA & \(O(nc)\) & 6 & no & no & yes \\ \hline GA & \(O(n^{2}c)\) & 6 & no & no & no \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of four stochastic algorithms. In [5], [PERSON] et al. proposed an algorithm to optimize the shape of the centrifugal blood pump based on the _genetic algorithm_. They applied the proposal to optimize the parameters of the CFD simulation to improve the performance. The results showed that the hydraulic efficiency was improved 11.1% and the hemolysis index was reduced 11.8% by using the optimized shape of the centrifugal blood pump. In [6], [PERSON] et al. propose the genetic algorithm (GA) to find the values of parameters used in _Deep Deterministic Policy Gradient (DDPG)_ combined with _Hindsight Experience Replay (HER)_, to help speed up the learning agent. They used this method to fetch, reach, slide, push, pick and place, and open a door in robotic manipulation tasks. They use GA to optimize four target parameters: the discounting factor \(\gamma\), the polyak-averaging coefficient \(\tau\), the learning rate for the critic network \(\alpha_{critic}\), the learning rate for the actor-network \(\alpha_{actor}\), the rate of times a random action is taken \(\epsilon\), and the standard deviation of Gaussian noise added to not completely random actions as a percentage of the maximum absolute value of actions on different coordinates \(\eta\). According to their experiments and results, \(\gamma\) decrease from 0.98 to 0.88, \(\tau\) decrease from 0.95 to 0.184, \(\alpha_{critic}\) and \(\alpha_{actor}\) keep 0.001, \(\epsilon\) decrease from 0.3 to 0.055, \(\eta\) increase from 0.2 to 0.774. These optimized parameters' values can speed up the learning agent. In [7], [PERSON] et al. propose an intrusion detection model based on an improved genetic algorithm (GA) and a deep belief network (DBN) to prevent the security of IoT. Facing different types of attacks, through multiple iterations of GA, the optimal number of hidden layers and number of neurons in each layer is generated adaptively, so that the intrusion detection model based on the DBN achieves a high detection rate with a compact structure. For the results, the detection accuracy for DOS attacks by using GA-DBN can arrive at 99.45%. In [8], [PERSON] et al. propose an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks.