You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Survey of Nearest Neighbor Condensing Techniques 
Chapter Title:  II. PROTOTYPE SELECTION 
1. Abstract of Cited Paper (BIB001):  Clustering is one of the most commonly techniques in Data Mining. Kmeans is one of the most popular clustering techniques due to its simplicity and efficiency. However, it is sensitive to initialization and easily trapped in local optima. K-harmonic means clustering solves the problem of initialization using a built-in boosting function, but it is suffering from running into local optima. Particle Swarm Optimization is a stochastic global optimization technique that is the proper solution to solve this problem. In this paper, PSOKHM not only helps KHM clustering escape from local optima but also overcomes the shortcoming of slow convergence speed of PSO. In this paper, a hybrid data clustering algorithm based on PSO and Genetic algorithm, GSOKHM, is proposed. We investigate local optima method in addition to the global optima in PSO, called LSOKHM. The experimental results on five real datasets indicate that LSOKHM is superior to the GSOKHM algorithm.

Literature Review:
Prototype selection is the process of finding representative patterns from the data, which can help in reducing these data. This problem is classified as an NP-hard problem by many researchers BIB002 BIB001 , because there is no polynomial algorithm allowing for the solution. The existing algorithms can just give acceptable solutions. Like many other combinatorial problems, the prototype selection (PS) would require an exhaustive search to obtain optimal solutions in the general case. This has led some researchers to consider the problem of PS as a combinatorial optimization problem and use general techniques which are known for their good results in similar situations. Heuristics, especially the nearest neighbor algorithm, and metaheuristics, especially genetic algorithms (GA) and tabu search (TS) have been proposed to solve this problem.