## 2 Related Work  
The idea of using a random number generator for initializing the population into a multidimensional search space is not new. An essential step in any metaheuristic algorithm is to initialize its population correctly. If the initialization is not proper, then it may go on to search in unnecessary areas and may fail to explore the optimum solution. Proper initialization is very important for the metaheuristic algorithm for its performance. Many research studies proposed different variants based on initialization techniques. Initialization of the swarm in a good way helps the PSO to search more efficiently [26]. In this work, the initialization of swarm with nonlinear simplex method (NSM) has been conducted. NSM requires only function evaluations without any derivatives for computation. NSM starts with initial simplex and produces sequence of steps moving the highest function value vertex in the opposite direction of the lowest one. They initialized the particle with the initial simplex in the D-dimensional search areas D+1, vertices of the simplex are D+1 particle of the swarm, and applied the MSN method for N-D+1 steps for N size swarm. In this way, each particle in the swarm has the information of the region. Lastly, they compared their results with simple PSO and found significant improvement. This variant was introduced by [PERSON] and [PERSON] in 2004. In their work [27], they proposed to use centroidal Voronoi tessellations for initializing the swarm. Voronoi tessellations is a technique of partitioning any region into compartments; each partition contains a group of generators. Each partition is associated with one generator, and it consists of all the particles closer to that generator. In the same way, the generators are selected for the initial position of the particle. In this way, they initialized the particle swarm optimization algorithm. They compared it with basic SPO on many benchmark functions and found improved performance in high-dimensional spaces. Halton sampling was introduced by [PERSON], [PERSON], and [PERSON] in 2007 [28,29]. Halton sequence is a low-discrepancy deterministic sequence used to generate a point in space. Halton sequence is not entirely random. To randomize X, [PERSON] and [PERSON] proposed a new function called randomize Halton sequence by using von Neumann-Kakutani transformation. They used this sequence to initialize the global best of the PSO. They performed a test on various benchmark functions and compared the result with the PSO and initialized with uniform random numbers. They found better performance, especially for complex and smaller populations. VC-PSO was introduced by [PERSON] et al. in 2008. They used Van der Corput sequence for initializing the swarm for large dimensions' search [30,31]. The Van [PERSON] and Sobol sequence generate a semirandom number, which is more suitable for computational purposes. They tested the new variant with different benchmark functions and compared the result with BPSO and SO-PSO and found significant improvement, especially for large search space dimensions. The main purpose of this variant is to see the performance of large search space problems. That is why they used search space with different sizes ranging [\(-\)5.12, 5.12] to [\(-\)1000, 1000]. The performance of the algorithm is showing prominence when the dimension increases to [\(-\)100, 100]. SMPO was introduced by [PERSON] et al. in 2008. They used a quasirandom Sobol sequence to initialize the particles instead of standard random numbers [25,32]. They used a new operator called a systematic mutation operator, which is used to improve the performance of the PSO. The new operator, instead of using the standard random number,use a quasirandom Sobol sequence to initialize the swarm as the QRS is less random as compared to pseudorandom sequences, which is helpful for computational methods. They proposed two variants SM-PSO1 and SM-PSO2.