1. Abstract Time requirements for the solving of complex large-scale engineering problems can be substantially reduced by using parallel computation. Motivated by a computationally demanding biomechanical system identification problem, we introduce a parallel implementation of a stochastic population based global optimizer, the Particle Swarm Algorithm as a means of obtaining increased computat...

Particle Swarm Classification (PSC) is a derivative of Particle Swarm Optimization (PSO) based on the retrieval of the best particle positions corresponding to the centroids of classes. This paper addresses how the position update mechanisms impacts the accuracy of a global best PSC approach. The authors present two variants of the PSC algorithm with different position update mechanisms. In par...

Aiming at industrial organization multi-objective optimization problem in Equipment Manufacturing Industry, The paper proposes a new type of double layer evolutionary cultural particle swarm optimization algorithm. The algorithm combines the advantages of the particle swarm optimization algorithm and cultural algorithm. It not only revises the problem that the particles are easy to “premature”,...

For improving the equalization performance of higher-order QAM signals, orthogonal Wavelet transform dynamic Weighted Multi-Modulus blind equalization Algorithm based on the Dynamic Particle Swarm Optimization(DPSO-WWMMA) is proposed. In this proposed algorithm, dynamic particle swarm optimization algorithm and orthogonal wavelet transform are introduced into dynamic Weighted Multi-Modulus blin...

This paper presents a novel adaptive multiobjective particle swarm optimization algorithm and with adaptive multi-objective particle swarm algorithm for solving objective constrained optimization problems, in which Pareto non-dominated ranking, tournament selection, crowding distance method were introduced, simultaneously the rate of crowding distance changing were integrated into the algorithm...

This paper proposes an energy efficient control strategy for an induction machine (IM) based on two advanced particle swarm optimisation (PSO) algorithms. Two advanced PSO algorithms, known as the dynamic particle swarm optimisation (Dynamic PSO) and the chaos particle swarm optimisation (Chaos PSO) algorithms modify the algorithm parameters to improve the performance of the standard PSO algori...

The particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergent speed around global optimum, and at the same time, the convergent accuracy can be higher. So in this paper, a hybrid algorithm comb...

This paper presents a short study on the hybridization of a swarm based optimization algorithm with a single agent based algorithm. Swarm based algorithms and single agent based algorithms have each distinct advantages and disadvantages. One goal of the presented work is to combine the concepts of the two different algorithms such that a more effective optimization routine results. In particula...

In order to improve the prediction ability of the Neuron Network in stock index prediction, we proposed an improve particle swarm neural network algorithm. The Particle Swarm Optimization algorithm based on Parasitic Immune (PSOPI) was used to optimize combination weights of BP neural network model parameters. The BP algorithm was also to obtain the parameters of network further accurate. Final...