## 2 Related Work  
PSO results, especially in convergence speed and quality of a final solution heavily depends on the quality of initial particles or particles initialization [26]. High quality initialized particles are achieved when they are more dispersed and closer to the global minimum. In many existing PSO variants, particles are initialized randomly. Initialization is a critical factor in the PSO algorithm that significantly affects diversity and convergence. Such an initialization has characteristics of slow convergence and become easily trapped in local optima [27]. This drawback is due to the lack of diversity in initialized particles. Hence, studies have been conducted to enhance the diversity of initialized particles. Tent or Logistic chaotic mapping is introduced by [28; 29; 30; 31; 32] to initialize uniformly distributed particles. This chaotic-based initialization achieved better results compared with random-based initialization. Refs. [33; 34] proposed an opposition-based population initialization by employing a symmetric strategy. This strategy can prevent initial particles from being distant from the global optimum. Opposition-based learning (OBL) is also employed in the work of [26], combined with Logistic chaos mapping. The experiments validate that this kind of initialization can recognize the search area better. Slightly different to others, [PERSON] [35] uses semi-random initialization by dividing the entire search space. The distribution of particles over a search space is performed in independents slots. The Weibull probability sequence is used to generate numbers at random locations for swarm initialization [36]. In the most recent study, [PERSON] [23] introduced uniform initialization (UI) strategy. UI strategy initializes a particle randomly as the basic point and then generates other initial particles based on this basic point. Hence, the initial particles are evenly distributed in each dimension, and the positions of each particle in each dimension are randomized. This mechanism can prevent the aggregation of initial particles. Ref. [37] proposed a pseudo-random initialization strategy called WELL on population initialization. This strategy imparted a significant effect on the importance of convergence and diversity. Maintaining a balance between exploration and exploitation is very important for an optimization algorithm. In this way, inertia weight plays a critical role to maintain this balance. In standard PSO (SPSO), inertia weights are based on linear decreasing weight, which lead to a poor result/balance. Therefore, several studies have been proposed to tackle this problem. Refs. [38; 39], proposed random-based inertia weights. This kind of randomness improved the population diversity in the early search phase, while it affects the local search ability in the later search phase, which reduces the convergence speed. In contrast, [PERSON] [40] and [PERSON] [41] introduced a dynamic inertia weight to replace the random inertia weight by controlling the convergence of the swarm towards a solution. An adaptive inertia weight based on the binomial probability distribution is proposed by [18], while [42] using the triangular probability density function to make the inertia weight generally greater in the initial stage of evolution, which is suitable for global search. Ref. [43] proposed an adaptive self-inertia weight with a gradient-based local search strategy. As stated by [44], for better performance, the inertia weight should be nonlinearly and dynamically changed to achieve better dynamics of balance between global and local search abilities. Recent studies proposed a chaotic-based inertia weight strategy. Ref. [45] used Sine map [46], Gauss map, and [47] Logistic map to adjust and tune the inertia weight. In SPSO, the velocity and position of each particle are updated using a learning mechanism based on its personal and population best experience.