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Top1. Introduction
Nowadays, most of the design optimization problems in engineering are turning out to be complicated due to involving mixed (discrete and continuous) variables under complex constraints. Generally, these problems are large scale non-linear constrained problems and hence cannot be solved by traditional methods efficiently. Presently, to overcome the drawbacks of conventional optimization methods, a bunch of optimization methods known as meta-heuristics algorithms (MAs) has been introduced. According to the mechanical differences the MAs can be categorized into four groups as follows- (i). Swarm intelligence algorithms (SIAs): inspired from behavior of social insects or animals like PSO (Particle Swarm Optimization) (Kennedy, & Eberhart, 1995), ABC (Artificial Bee Colony Algorithm) (Karaboga, & Basturk, 2007), FA (Firefly Algorithm) (Yang, 2009), CS (Cuckoo Search) (Yang & Deb, 2009), KH (Krill Herd) (Gandomi & Alavi, 2012), GWO (Grey Wolf Optimizer) (Mirjalili, Mirjalili, & Lewis 2014), DA (Dragonfly Algorithm) (Mirjalili, 2016), WOA (Whale Optimization Algorithm) (Gadekallu et al., 2020) etc. (ii). Evolutionary algorithms (EAs): inspired from biology like GA (Genetic Algorithm) (Davis, 1991) and DE (Differential Evolution) (Storn & Price, 1997) etc. (iii). Physics-based algorithms (PBAs): inspired by the rules governing a natural phenomenon such as HS (Harmony Search) (Geem, Kim, & Loganathan, 2001), GSA (Gravitational Search Algorithm) (Rashedi, Nezamabadi-pour, & Saryazdi, 2009), WCA (Water Cycle Algorithm) (Eskandar, Sadollah, Bahreininejad, & Hamdi, 2012), WDO (Wind Driven Optimization) (RM et al. 2020) and so on. (iv). Human behavior-based algorithms (HBAs): inspired by a human being like TLBO (Teaching-learning-based optimization) (Rao, Savsani, & Vakharia, 2011), SAR (Search and rescue optimization) (Shabani, Asgarian, Gharebaghi, Salido, & Giret, 2019) etc.
Among many MAs, DE and PSO have been widely used in continuous/discrete, constrained as well as unconstrained optimization problems. DE has remarkable performance and becomes a powerful optimizer in the field of real-world problems. However, it has a few issues such as convergence rate and local exploitation ability. In order to overcome its shortcomings, lots of robust and effective DE has been designed in the literature (Yang, & Peng, 2019; Prabha, & Yadav, 2019; Liu, Ji, & Yang, 2019; Gui, Xia, Yu, Wu, Wu, Wei, & He, 2019; Li, Gu, Gong, & Ning, 2020; Hu, Hua, Lei, & Xiantian, 2020; Ben, 2020). Also, PSO has attracted attention to solving many complex optimization problems due to its efficient searchability and simplicity. However, the main drawback of the PSO is that it may easily get stuck at a locally optimal solution region. To overcome such issues many different modifications of the PSO proposed in the literature (Parouha, 2019; Hosseini, Hajipour, & Tavakoli, 2019; Kohler, Vellasco, & Tanscheit, 2019; Khajeh, Ghasemi, & Arab, 2019; Ang, Lim, Isa, Tiang, & Wong, 2020; Lanlan, Ruey, Wenliang, & Yeh, 2020; Xiong, Qiu, & Liu, 2020). Furthermore, a hybrid strategy is one of the main research directions to improve the performance of single algorithm (Maddikunta et al., 2020). Therefore, in order to enhance the performance of DE and PSO, lots of their hybrid algorithms are presented in the literature (Parouha, & Das, 2016; Mao, Xie, Wang, Handroos, & Wu, 2018; Tang, Xiang, & Pang, 2018; Too, Abdullah, & Saad, 2019; Dash, Dam, & Swain, 2019; Zhao, Zhang, Xie, & Meng, 2020). Nevertheless, to overcome their individual shortcomings, hybrid techniques are now more favored over their individual effort.