Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey <s> 2) Multi-swarm and multi-best particle swarm optimization algorithm <s> Center particle swarm optimization algorithm (CenterPSO) is proposed where a center particle is incorporated into linearly decreasing weight particle swarm optimization (LDWPSO). Unlike other ordinary particles in LDWPSO, the center particle has no explicit velocity, and is set to the center of the swarm at every iteration. Other aspects of the center particle are the same as that of the ordinary particle, such as fitness evaluation and competition for the best particle of the swarm. Because the center of the swarm is a promising position, the center particle generally gets good fitness value. More importantly, due to frequent appearance as the best particle of swarm, it often attracts other particles and guides the search direction of the whole swarm. CenterPSO and LDWPSO are extensively compared on three well-known benchmark functions with 10, 20, 30 dimensions. Experimental results show that CenterPSO achieves not only better solutions but also faster convergence. Furthermore, CenterPSO and LDWPSO are compared as neural network training algorithms. The results show that CenterPSO achieves better performance than LDWPSO. <s> BIB001 </s> Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey <s> 2) Multi-swarm and multi-best particle swarm optimization algorithm <s> 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. <s> BIB002
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.