Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey <s> d) Cooperative Particle Swarm Optimization <s> The standard particle swarm optimization (PSO) algorithm, existing improvements and their influence to the performance of standard PSO are introduced. The framework of PSO basic formula is analyzed. Implied by its three-term structure, the inherent shortcoming that trends to local optima is indicated. Then a modified velocity updating formula of particle swarm optimization algorithm is declared. The addition of the disturbance term based on existing structure effectively mends the defects. The convergence of the improved algorithm is analyzed. Simulation results demonstrated that the improved algorithm have a better performance than the standard one. <s> BIB001 </s> Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey <s> d) Cooperative Particle Swarm Optimization <s> This paper introduces a modified particle swarm algorithm to handle multiobjective optimization problems. In multiobjective PSO algorithms, the determination of Pareto optimal solutions depends directly on the strategy of assigning a best local guide to each particle. In this work, the PSO algorithm is modified to assign a best local guide to each particle by using minimum angular distance information. This algorithm is implemented to determine field-effect transistor (FET) model elements subject to the Pareto domination between the scattering parameters and operation bandwidth. Furthermore, the results are compared with those obtained by the nondominated sorting genetic algorithm-II. FET models are also built for the 3 points sampled from the different locations of the Pareto front, and a discussion is presented for the Pareto relation between the scattering parameter performances and the operation bandwidth for each model. <s> BIB002
A modified particle swarm optimizer named Cooperative PSO (CPSO) was proposed by Van denBergh and Engelbrecht BIB002 . The CPSO could significantly improve the performance of the original PSO by utilizing multiple swarms for optimizing different components of the solution vector by employ in cooperative behavior. In this method, the search space is partitioned by dividing the solution vectors into small vectors, based on the partition several swarms will be randomly generated in different parts of the search space and used to optimize different parts of the solution vector. BIB001 presented PSO with disturbance term (PSO-DT) which add a disturbance term to the velocity updating equation based on the prototype of the standard PSO trying to improve (or avoid) the shortcoming of standard PSO. The addition of the disturbance term based on existing structure effectively mends the defects. The convergence of the improved algorithm was analyzed. Simulation results demonstrated that the improved algorithm have a better performance than the standard one.