## 2 Related Works
### Improved Differential Evolution
The hybrid uses the selection probability of mutation strategy to decide between the modified DE/rand/1 and the PSO's mutation strategy. The modified mutation strategy with the elite archive strategy, called DE/e-rand/1, is proposed in DEPSO. The framework of DEPSO is still based on DE. Unlike DEPSO, our proposed FIPSaDE is based on the framework of PSO. The hybridizationof DEPSO focuses on selecting mutation strategies from DE and PSO to generate the mutant vector. Therefore, its hybridization involves the mutation phase only. On the other hand, the hybridization in FIPSaDE involves integrating DE's algorithmic operations, from the mutation to selection phases, into the framework of PSO before the velocity and position updating. [PERSON] et al. proposed HDEPSO [22] to solve various benchmark functions and an optimization problem focusing on the effectiveness of the sharp edge FIR filter (SEFIRF). The proposed framework is similar to FIPSaDE, whereby, DE's mutation, crossover and selection are integrated with the best particles of PSO to enhance global searching ability. However, the settings of \(F\) and \(C\), were not adaptive. \(F\) is fixed as a constant and \(C\), refers to an arbitrary number between \([0,1]\). In contrast, the settings of \(F\) and \(C\), in FIPSaDE are adaptive. For the case of the hybrid approach, it may cause more uncertainties in parameter setting in view of the fact that a user needs to realize how to set parameters for at least two algorithms [23]. The task of parameter setting in solving optimization problem is problem-dependent, user-dependent and algorithm-dependent. The task becomes more complex when the algorithms of the hybrid are from different branches of machine learning. Therefore, researchers start to focus on the possibility of applying adaptation methods for users that have minimum knowledge about the algorithms, parameters settings and the problems. DE variants with varying adaptation levels have shown promising results in solving optimization problems and its popularity in setting DE's parameters can be found in the aforementioned reviews. For example, the work in [24] showed the use of adaptation to tune the configurations of \(F\), \(C\), and mutation strategy at different stages of the evolution. Another work demonstrating the use of adaptation in controlling parameters \(F\), \(C\), is the Success-History-based adaptive DE (SHADE) algorithm [25]. SHADE uses the nearest spatial neighborhood-based modification to the adaptation process of the parameters described above. [PERSON] et al. [26] also proposed an adaptive mechanism to determine the parameters \(F\) and \(F\), \(C_{r}\) with the mutation and selection processes determined by the best individual-based mutation and elitist selection techniques. The adaptation in population sizing is summarized in the review by [PERSON] [27]. These research studies have shown the use of adaptation to control DE's various parameters. The adaptability trend to set DE's parameters, from partial adaptiveness to full adaptiveness, is increasing. Since the adaptive DE variants have shown promising results, its use to form the hybrid can reduce the complexity of parameter setting because the number of algorithms that require user-specified parameters has declined. Therefore, we use a adaptive DE called SaDE in [8] to form the hybrid of DE and PSO in the current work. SaDE adapts the control parameter \(F\) and \(C_{r}\) based on the mutation strategy \(DE/rand/1/binstrategy\).