## 2 Related Works  
### Improved Differential Evolution  
DE is one of the most popular evolutionary algorithms used to solve optimization problems because it is simple, robust and computationally efficient. However, DE faces issues in convergence rate and local exploitation rate [9]. Therefore, various research efforts for its improvement are continuously carried out even though the algorithm has been introduced 25 years ago by [PERSON] and [PERSON] [10]. Besides, surveys related DE variants are updated after a certain years to accommodate various modifications of DEs, as shown in [5; 9; 11; 12; 13; 14; 15]. The surveys show that most of the research efforts focus on improving the performance of DE through parameter settings and modifications in genetic operations consisting of initialization, differential mutation, crossover and selection. Another potential direction towards improving DE is the use of hybridization, and it has gained research attention [9; 11; 12]. Most of the optimization algorithms are not able to solve a variety of problems [9]. However, continuous research efforts are required to improve the algorithm through the complementary of their advantages. Hybridization is one the main research directions in improving the performance of DE because different optimization algorithms have different search behaviours and advantages [16]. It is implemented to enhance its performance and overcome its limitations such as convergence speed [11], premature convergence [9] and local minima [9]. The types of algorithms used for hybridization in DE can either belong to the same or different categories of algorithms. The work in [12] categorizes the algorithm in hybridization of DE into statistical techniques and algorithms. On the other hand, the work in [11] shows that most of the algorithms in a hybridization of DE are from the swarm intelligence algorithms. The commonly used swarm intelligence algorithms to hybridize with DE include PSO, ant colony algorithm (ACA) and artificial bee colony (ABC). DE variants produce robust solutions owing to their exploration ability. However, they have issues related to premature convergence and local exploitation. On the other hand, one of the drawbacks of PSO is premature convergence [17]. Premature convergence is caused by improper velocity adjustment when PSO is configured by inappropriate acceleration coefficient and inertia weights [18]. Consequently, the particles move in undesired directions, causing stagnation around or being trapped in suboptimal [18]. A review of the modifications, extensions and hybridization of the PSO algorithm and their applications is presented in [19]. Most of the hybrid variants of DE is formed with PSO [11] and an extensive survey about the hybrid can be found in [9]. A hybrid of advanced DE (ADE) and (PSO) (APSO), namely, AHDEPSO, was proposed to solve unconstrained optimization problems in [9]. Given that a hybrid of DE and PSO offers complementary properties and has the effect of balancing the exploration and exploitation phases, the hybrid of their advanced variants has attracted more research attention. The research focusing on how to combine PSO and DE is still an open problem [16]. The premature convergence of PSO in the optimization process can be improved based on four different methods: parameter settings, neighborhood topology, learning strategy and hybridization [20]. Neighbourhood topology enhances PSO's exploration capability and different topologies affected solve different optimization problems [20]. On the other hand, hybridization is used to complement the weakness of intelligent algorithms by combining their helpful features. Therefore, FIPS, instead of the original PSO, is hybridized with DE to improve the performance of the optimization algorithms. The self-adaptive mutation differential evolution algorithm based on particle swarm optimization (DEPSO) [21] uses balance to improve the optimization.