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: Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey 
Chapter Title:  2) Multi-swarm and multi-best particle swarm optimization algorithm 
1. Abstract of Cited Paper (BIB001):  Competitive bidding for energy and ancillary services is increasingly recognized as an important part of electricity markets. In addition, the transmission capacity limits should be considered to optimize the total market cost. In this paper, a new approach based on constrained particle swarm optimization (CPSO) is developed to deal with the multi-product (energy and reserve) and multi-area electricity market dispatch problem. Constraint handling is based on particle ranking and uniform distribution. CPSO method offers a new solution for optimizing the total market cost in a multi-area competitive electricity market considering the system constraints. The proposed technique shows promising performance for smooth and non smooth cost function as well. Three different systems are examined to demonstrate the effectiveness and the accuracy of the proposed algorithm.

Literature Review:
A new method named Multi-Best PSO (MBPSO) is proposed BIB001 . This method instead of using single global best position (gbest) and personal best position (Pbest), it uses the multi gbest and multi Pbest. So in the course of searching, other best values can help the best value trapped by local optimum fly out of local position. MBPSO divided the whole population into the sub-swarms and then calculates the several gbest and then combines all particles together and then calculates again taking the result as a new initial value. 3) Dynamic multi-swarm particle swarm optimizerwith sub-regional harmony search DMS-PSO-SHS BIB002 is an extension of DMS-PSO that divided the whole of the population into the small sub-swarm with dynamic size to adopt each one the population of the harmony search algorithm. This method based on the DMS-PSO, generate new harmonies according to the current personal best solution and the nearer personal best solution is replaced with a new harmony with better fitness. The DMS-PSO-SHS enables the particles to have more diverse exemplars to learn from after we frequently regroup the swarms and allow the harmonies to search in a larger potential space among different sub-populations.