## 2 Related Work
The main difference between the two versions is that in MSPSO1, the best particle is mutated, while in MS-PSO2, the worst particle is mutated. They found better results compared to BPSO and other variants. This work is done by [33]. In this paper, researchers have proposed a new method of initialization. In their work, they have added the functionality to automatically detect when a particle is prematurely converged and initializes the swarm. They also added features to redesign the inertia weight to balance the searching ability globally and locally. They named it as IAWPSO. This variant is proposed by [PERSON] in 2012 and applied on the transmission expansion problem to decide the installation of new circuits in increased usage of electricity and found this variant fruitful [34]. In this work, he used a new initialization technique called population monitored for complementary magnitudes initialization. In initialization, he used decision variables. All particles are initialized with an integer within the limit of the upper and lower values of the decision variable in such a way that each particle should be unique. The initial population is created in such a way that each particle can have the ability of the possible solution, and they are unique. Almost 50% of the particles are opposite to another 50%, considering the lower and upper limit of the decision variable. The important thing in this initialization is to maintain uniqueness and diversity among the particles of the swarm generated initially. SIS_PSO was introduced by [35]. In this paper, the authors introduced a new initialization technique named space-based initialization strategy. In this work, they broke down each dimension of the search area into two segments: S1i and S2i. The border of the areas is [li, (li + ui)/2] and [(li + ui)/2, ui], and each segment is linked with a probability and initialized with 0.5. They applied SIS-PSO on 13 functions and compared the result with GPSO and CLPSO and found significant improvement. [PERSON] et al. [36] describes that problem of finding the global minima or maxima in single real variable f(x) of continuous function is an equally important research domain for science and engineering. A number of methods have been proposed so far to properly solve this problem of finding global minima or maxima. All these methods have been generally categorized into two sets or groups as deterministic or stochastic. In this paper, the author catered to these problems by introducing a novel approach as mix of both deterministic and stochastic approaches, by finding the precision of the deterministic approaches along with the localization ability of stochastic approaches. The proposed novel approach is aligned with genetic algorithm (GA). This variant was introduced by [37]. In this work, they introduced a new variant of PSO called polar PSO. They explained that mostly the distortion occurred due to the polar particles. Hence, they introduced a new method for the reinitialization of the polar particles by redefining the distance based on the dimensionality of the point. By using this method, they removed the distortion occurring during the computation. They compared the results with BPSO and found some improvement. This variant was proposed by [38]. In this work, they used the Nawaz-Enscore-Ham heuristic technique to initialize the swarm. This variant is named PHPSO. The sequence generated by NEH jobs is placed in ascending order of the sums of their total flow time. To construct a job sequence, it depends on its initial order. The minimum TFT sequence is the current sequence for the upcoming iteration among all the sequences. The resulting population generated by the NEH method is used to initialize the population of PSO. They applied this algorithm for the no-wait flow shop scheduling problem. They compared the result with DPSO and HPSO and found a comparatively better result. This work is done by [39]. They proposed a new algorithm combining Adoptive PSO with backpropagation (BP) to train weights of neural network.