Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey <s> a) Linear Decreasing Weight Particle Swarm Optimization <s> A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described. <s> BIB001 </s> Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey <s> a) Linear Decreasing Weight Particle Swarm Optimization <s> The standard Particle Swarm Optimizer may prematurely converge on suboptimal solutions that are not even guaranteed to be local extrema. A new particle swarm optimizer, called stochastic PSO, which is guaranteed to convergence to the global optimization solution with probability one, is presented based on the analysis of the standard PSO. And the global convergence analysis is made using the F.Solis and R.Wets’ research results. Finally, several examples are simulated to show that SPSO is more efficient than the standard PSO. <s> BIB002
Linear Decreasing Weight particle swarm optimization (LDWPSO) algorithm was presented by Shi and Eberhart BIB002 . The inertia weight w is decreased linearly over the searching iterations, from an initial value to a final value as follows: (5) Where w is the inertia weight that controls the velocity of particles, w max is the initial inertia weight, w min is the final inertia weight, Max. Iter is the maximum number of iterations, www.ijacsa.thesai.org and Iter is the current iteration. LDWPSO algorithm uses equation BIB001 to update position, equation (2) to update velocity and equation (5) to update the inertia weight.